{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6.6 Lab 2: Ridge Regression and the Lasso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-07T12:09:16.655258Z",
     "start_time": "2023-10-07T12:09:16.502324Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.linear_model import Ridge,Lasso\n",
    "from sklearn.model_selection import train_test_split,GridSearchCV\n",
    "from sklearn.metrics import mean_squared_error,r2_score\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from itertools import combinations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-07T12:09:16.683001Z",
     "start_time": "2023-10-07T12:09:16.655802Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(322, 21)\n"
     ]
    },
    {
     "data": {
      "text/plain": "          Unnamed: 0  AtBat  Hits  HmRun  Runs  RBI  Walks  Years  CAtBat  \\\n0     -Andy Allanson    293    66      1    30   29     14      1     293   \n1        -Alan Ashby    315    81      7    24   38     39     14    3449   \n2       -Alvin Davis    479   130     18    66   72     76      3    1624   \n3      -Andre Dawson    496   141     20    65   78     37     11    5628   \n4  -Andres Galarraga    321    87     10    39   42     30      2     396   \n\n   CHits  ...  CRuns  CRBI  CWalks  League Division PutOuts  Assists  Errors  \\\n0     66  ...     30    29      14       A        E     446       33      20   \n1    835  ...    321   414     375       N        W     632       43      10   \n2    457  ...    224   266     263       A        W     880       82      14   \n3   1575  ...    828   838     354       N        E     200       11       3   \n4    101  ...     48    46      33       N        E     805       40       4   \n\n   Salary  NewLeague  \n0     NaN          A  \n1   475.0          N  \n2   480.0          A  \n3   500.0          N  \n4    91.5          N  \n\n[5 rows x 21 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Unnamed: 0</th>\n      <th>AtBat</th>\n      <th>Hits</th>\n      <th>HmRun</th>\n      <th>Runs</th>\n      <th>RBI</th>\n      <th>Walks</th>\n      <th>Years</th>\n      <th>CAtBat</th>\n      <th>CHits</th>\n      <th>...</th>\n      <th>CRuns</th>\n      <th>CRBI</th>\n      <th>CWalks</th>\n      <th>League</th>\n      <th>Division</th>\n      <th>PutOuts</th>\n      <th>Assists</th>\n      <th>Errors</th>\n      <th>Salary</th>\n      <th>NewLeague</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>-Andy Allanson</td>\n      <td>293</td>\n      <td>66</td>\n      <td>1</td>\n      <td>30</td>\n      <td>29</td>\n      <td>14</td>\n      <td>1</td>\n      <td>293</td>\n      <td>66</td>\n      <td>...</td>\n      <td>30</td>\n      <td>29</td>\n      <td>14</td>\n      <td>A</td>\n      <td>E</td>\n      <td>446</td>\n      <td>33</td>\n      <td>20</td>\n      <td>NaN</td>\n      <td>A</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>-Alan Ashby</td>\n      <td>315</td>\n      <td>81</td>\n      <td>7</td>\n      <td>24</td>\n      <td>38</td>\n      <td>39</td>\n      <td>14</td>\n      <td>3449</td>\n      <td>835</td>\n      <td>...</td>\n      <td>321</td>\n      <td>414</td>\n      <td>375</td>\n      <td>N</td>\n      <td>W</td>\n      <td>632</td>\n      <td>43</td>\n      <td>10</td>\n      <td>475.0</td>\n      <td>N</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>-Alvin Davis</td>\n      <td>479</td>\n      <td>130</td>\n      <td>18</td>\n      <td>66</td>\n      <td>72</td>\n      <td>76</td>\n      <td>3</td>\n      <td>1624</td>\n      <td>457</td>\n      <td>...</td>\n      <td>224</td>\n      <td>266</td>\n      <td>263</td>\n      <td>A</td>\n      <td>W</td>\n      <td>880</td>\n      <td>82</td>\n      <td>14</td>\n      <td>480.0</td>\n      <td>A</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>-Andre Dawson</td>\n      <td>496</td>\n      <td>141</td>\n      <td>20</td>\n      <td>65</td>\n      <td>78</td>\n      <td>37</td>\n      <td>11</td>\n      <td>5628</td>\n      <td>1575</td>\n      <td>...</td>\n      <td>828</td>\n      <td>838</td>\n      <td>354</td>\n      <td>N</td>\n      <td>E</td>\n      <td>200</td>\n      <td>11</td>\n      <td>3</td>\n      <td>500.0</td>\n      <td>N</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>-Andres Galarraga</td>\n      <td>321</td>\n      <td>87</td>\n      <td>10</td>\n      <td>39</td>\n      <td>42</td>\n      <td>30</td>\n      <td>2</td>\n      <td>396</td>\n      <td>101</td>\n      <td>...</td>\n      <td>48</td>\n      <td>46</td>\n      <td>33</td>\n      <td>N</td>\n      <td>E</td>\n      <td>805</td>\n      <td>40</td>\n      <td>4</td>\n      <td>91.5</td>\n      <td>N</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 21 columns</p>\n</div>"
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(r'../data/Hitters.csv')\n",
    "print(data.shape)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-07T12:09:16.683408Z",
     "start_time": "2023-10-07T12:09:16.672041Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "['Salary']"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[col for col in data.columns if data[col].isnull().sum()>0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-07T12:09:16.712684Z",
     "start_time": "2023-10-07T12:09:16.674527Z"
    }
   },
   "outputs": [],
   "source": [
    "# ONly Salarty has missing values\n",
    "data.dropna(inplace = True)\n",
    "# also we don;t need the name col\n",
    "data.drop('Unnamed: 0',axis = 1,inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-07T12:09:16.747684Z",
     "start_time": "2023-10-07T12:09:16.680870Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['League', 'Division', 'NewLeague']\n"
     ]
    }
   ],
   "source": [
    "# In R, the qualitative variables are automatically converted into dummy variables, but here, we have to do it manually.\n",
    "qual_vars = [col for col in data.columns if data[col].dtype == 'O']\n",
    "print(qual_vars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-07T12:09:16.770536Z",
     "start_time": "2023-10-07T12:09:16.688287Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "   AtBat  Hits  HmRun  Runs  RBI  Walks  Years  CAtBat  CHits  CHmRun  CRuns  \\\n1    315    81      7    24   38     39     14    3449    835      69    321   \n2    479   130     18    66   72     76      3    1624    457      63    224   \n3    496   141     20    65   78     37     11    5628   1575     225    828   \n4    321    87     10    39   42     30      2     396    101      12     48   \n5    594   169      4    74   51     35     11    4408   1133      19    501   \n\n   CRBI  CWalks  PutOuts  Assists  Errors  Salary  League_N  Division_W  \\\n1   414     375      632       43      10   475.0      True        True   \n2   266     263      880       82      14   480.0     False        True   \n3   838     354      200       11       3   500.0      True       False   \n4    46      33      805       40       4    91.5      True       False   \n5   336     194      282      421      25   750.0     False        True   \n\n   NewLeague_N  \n1         True  \n2        False  \n3         True  \n4         True  \n5        False  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>AtBat</th>\n      <th>Hits</th>\n      <th>HmRun</th>\n      <th>Runs</th>\n      <th>RBI</th>\n      <th>Walks</th>\n      <th>Years</th>\n      <th>CAtBat</th>\n      <th>CHits</th>\n      <th>CHmRun</th>\n      <th>CRuns</th>\n      <th>CRBI</th>\n      <th>CWalks</th>\n      <th>PutOuts</th>\n      <th>Assists</th>\n      <th>Errors</th>\n      <th>Salary</th>\n      <th>League_N</th>\n      <th>Division_W</th>\n      <th>NewLeague_N</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>315</td>\n      <td>81</td>\n      <td>7</td>\n      <td>24</td>\n      <td>38</td>\n      <td>39</td>\n      <td>14</td>\n      <td>3449</td>\n      <td>835</td>\n      <td>69</td>\n      <td>321</td>\n      <td>414</td>\n      <td>375</td>\n      <td>632</td>\n      <td>43</td>\n      <td>10</td>\n      <td>475.0</td>\n      <td>True</td>\n      <td>True</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>479</td>\n      <td>130</td>\n      <td>18</td>\n      <td>66</td>\n      <td>72</td>\n      <td>76</td>\n      <td>3</td>\n      <td>1624</td>\n      <td>457</td>\n      <td>63</td>\n      <td>224</td>\n      <td>266</td>\n      <td>263</td>\n      <td>880</td>\n      <td>82</td>\n      <td>14</td>\n      <td>480.0</td>\n      <td>False</td>\n      <td>True</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>496</td>\n      <td>141</td>\n      <td>20</td>\n      <td>65</td>\n      <td>78</td>\n      <td>37</td>\n      <td>11</td>\n      <td>5628</td>\n      <td>1575</td>\n      <td>225</td>\n      <td>828</td>\n      <td>838</td>\n      <td>354</td>\n      <td>200</td>\n      <td>11</td>\n      <td>3</td>\n      <td>500.0</td>\n      <td>True</td>\n      <td>False</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>321</td>\n      <td>87</td>\n      <td>10</td>\n      <td>39</td>\n      <td>42</td>\n      <td>30</td>\n      <td>2</td>\n      <td>396</td>\n      <td>101</td>\n      <td>12</td>\n      <td>48</td>\n      <td>46</td>\n      <td>33</td>\n      <td>805</td>\n      <td>40</td>\n      <td>4</td>\n      <td>91.5</td>\n      <td>True</td>\n      <td>False</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>594</td>\n      <td>169</td>\n      <td>4</td>\n      <td>74</td>\n      <td>51</td>\n      <td>35</td>\n      <td>11</td>\n      <td>4408</td>\n      <td>1133</td>\n      <td>19</td>\n      <td>501</td>\n      <td>336</td>\n      <td>194</td>\n      <td>282</td>\n      <td>421</td>\n      <td>25</td>\n      <td>750.0</td>\n      <td>False</td>\n      <td>True</td>\n      <td>False</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.get_dummies(data,columns= qual_vars,drop_first=True)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6.6.1 Ridge Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-07T12:09:16.896053Z",
     "start_time": "2023-10-07T12:09:16.700254Z"
    }
   },
   "outputs": [],
   "source": [
    "list_alpha = 10**np.linspace(-2,10,100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-07T12:09:16.896681Z",
     "start_time": "2023-10-07T12:09:16.706907Z"
    }
   },
   "outputs": [],
   "source": [
    "# in R, by default, the predictors are standardized\n",
    "X = data.drop('Salary',axis = 1)\n",
    "y = data['Salary']\n",
    "scaler = StandardScaler()\n",
    "X = scaler.fit_transform(X)\n",
    "# Now, X is standardised"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-07T12:09:17.023913Z",
     "start_time": "2023-10-07T12:09:16.713779Z"
    }
   },
   "outputs": [],
   "source": [
    "coeff_matrix = {}\n",
    "\n",
    "for alpha in list_alpha:\n",
    "    model = Ridge(alpha=alpha)\n",
    "    model.fit(X,y)\n",
    "    coeff_matrix[alpha] = list(model.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-07T12:09:17.086593Z",
     "start_time": "2023-10-07T12:09:16.753716Z"
    }
   },
   "outputs": [
    {
     "data": {
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      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>0</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n      <th>5</th>\n      <th>6</th>\n      <th>7</th>\n      <th>8</th>\n      <th>9</th>\n      <th>10</th>\n      <th>11</th>\n      <th>12</th>\n      <th>13</th>\n      <th>14</th>\n      <th>15</th>\n      <th>16</th>\n      <th>17</th>\n      <th>18</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1.000000e-02</th>\n      <td>-290.918728</td>\n      <td>337.229910</td>\n      <td>37.495419</td>\n      <td>-60.017790</td>\n      <td>-26.665375</td>\n      <td>134.928045</td>\n      <td>-17.051824</td>\n      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     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp = pd.DataFrame(coeff_matrix).T\n",
    "tmp.index = list_alpha\n",
    "tmp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-07T12:09:17.657567Z",
     "start_time": "2023-10-07T12:09:16.759966Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.legend.Legend at 0x14f23d0a0>"
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 1600x800 with 1 Axes>",
      "image/png": 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J3W7n3HPPJTs7m1dffTVVhyEyIEzNyuH2abPJsjvY1NbCr5bOpzIUTHUsEREREREREenHbKkO0BV1dXVcc801PPjggzz66KM7ny8pKWHs2LG7jB09ejTPPPMMABs2bOCUU075wutr167tcgbD6Hru/mDHcXXm+Loytr/OrRydHz/an8GdM/bl2uWLqAiHuHzZfG6aOpNAwD9gjnGg5OjNuZWj7+ZOlxy9Obdy9N3c6ZKjN+dOlxy9Obdy9N3c6ZKjN+dWjr6bO11y9ObcytF3c6dLjt6cO11y9ObcXc3RH3X6OzZN0+zdKD0jmUxy/vnnc/jhh+9y/crbb7+da665hlgsxh133LFz/NNPP82f//xn3nrrLebOncuPfvQjTj311J2vX3755TgcDm655ZY+PxaRgag2FOKnH37IhqYmfHY7vzvgAGYEAqmOJSIiIiIiIiL9TL9ZYfmnP/0Jh8PB2Wef/YXX3G43LS0tuzwXDofxer07Xw+Hw194PTs7u8s56upa6B8Vb9cYBuTm+jt1fF0Z21/nVo7ujb9l8j78ZuUSVjU18tP33+c3U/dhcmZOn+dIt7nTJUdvzq0cfTd3uuTozbmVo+/mTpccvTl3uuTozbmVo+/mTpccvTm3cvTd3OmSozfnVo6+mztdcvTm3OmSozfn7mqO/mjHMX6dflNYvvDCC1RXVzNr1iyAnQXk22+/za9+9Ss+/PDDXcZv2LCBMWPGADBmzBhKSkq+8PohhxzS5RymyYD9RQNdO76ufhf9cW7l6Np4r9XObybP5LY1y1hYX8sNK5Zw29TZjPZn9GmOdJ07XXL05tzK0Xdzp0uO3pxbOfpu7nTJ0Ztzp0uO3pxbOfpu7nTJ0ZtzK0ffzZ0uOXpzbuXou7nTJUdvzp0uOXpz7q7mGIj6zaY7r7/+OosXL2bhwoUsXLiQ448/nuOPP56FCxcyd+5camtrefTRR4nFYsybN4+XXnpp53UrTz31VF566SXmzZtHLBbj0Ucfpa6ujrlz56b4qEQGHqfVytUTp7FPIEAokeC6FYvY1taa6lgiIiIiIiIi0k/0m8Lyq2RnZ/PXv/6V119/nf32249rr72Wa6+9lv333x+AOXPmcP3113PDDTew77778sorr/Dwww+TlZWV2uAiA5TTauXOAw5grD+D5niMa1cs0u7hIiIiIiIiItIp/eaU8M+7/fbbd3k8ZcoUnnzyyS8df9JJJ3HSSSf1diwR6eCz27lxyj5csXQB24JtXLtiEb+dNptcpyvV0UREREREREQkjQ2IFZYikp4y7A5unjKLQpebynCIX69YRHMsmupYIiIiIiIiIpLGVFiKSK/KcTq5ecpMchxOtgXbuH7FYoLxeKpjiYiIiIiIiEiaUmEpIr2u0O3h5ikzybDZKWlt5jerlhBJJFIdS0RERERERETSkApLEekTQ70+fjNlJh6rjZVNDdy2ZhmxZDLVsUREREREREQkzaiwFJE+M9qfwXWTZ+C0WFhYX8vv160kYZqpjiUiIiIiIiIiaUSFpYj0qcmZ2Vw9cTo2w+D9mkoeWL8aU6WliIiIiIiIiHRQYSkifW5mToDLxk/BArxRuZ17V6xQaSkiIiIiIiIigApLEUmRg/IKuXjsJAAeLynh2bKtKU4kIiIiIiIiIulAhaWIpMzRhcWcP3IsAH/btJ4F9TUpTiQiIiIiIiIiqabCUkRS6luDh/GtESMwgTvWLGdbW2uqI4mIiIiIiIhICqmwFJGUMgyDy6ZPZ0pmNqFEgt+sWkJzLJrqWCIiIiIiIiKSIiosRSTl7BYLV02aRoHLTWU4xG2rlxFPJlMdS0RERERERERSQIWliKSFTLuDX0+ajttqZUVTA3/cuFY7h4uIiIiIiIjshVRYikjaGO71c9n4KRjA6xVlvFJRmupIIiIiIiIiItLHVFiKSFrZLzef748YA8CfN6xjaUNdihOJiIiIiIiISF9SYSkiaeeUwcM5PH8QSUxuX7OM7aG2VEcSERERERERkT6iwlJE0o5hGFwydiLj/Jm0xuPctHIJrfFYqmOJiIiIiIiISB9QYSkiaclhsXLtpOkEHE7KQkHuWLOchKmdw0VEREREREQGOhWWIpK2sh1Orp00A6fFwuKGOv66qSTVkURERERERESkl6mwFJG0Ntqfwc/HTQbghe1bebNie4oTiYiIiIiIiEhvUmEpImnvoLxCvjN0JAAPlKxmaW1tihOJiIiIiIiISG9RYSki/cJ3ho3iwEABcdPkqnnzqI9GUh1JRERERERERHqBCksR6RcshsHPx01imNdHfSTC79asIGGaqY4lIiIiIiIiIj1MhaWI9Bsuq40rJ0zFZbWyrLGef23blOpIIiIiIiIiItLDVFiKSL8y1OvjVzNmAPDE1o2saKxPcSIRERERERER6UkqLEWk3zlu2DCOLCgiCdy5dgVN0WiqI4mIiIiIiIhID1FhKSL90oVjxjPY7aU+GuH361aQ1PUsRURERERERAYEFZYi0i+5rTaunDgVh8XCooY6ni3bkupIIiIiIiIiItIDVFiKSL813OvnR6PGA/D3zRtY3dSQ4kQiIiIiIiIisqdUWIpIv3Z0YTGH5hWSxOTOtStojul6liIiIiIiIiL9mQpLEenXDMPgojETKXJ7qImEuXvdKkxdz1JERERERESk31JhKSL9nsdm48oJU7EbFubX1/D89q2pjiQiIiIiIiIi3aTCUkQGhJG+DP5v1DgAHt1cwrrmphQnEhEREREREZHusKU6gKQH1/N3Ed68DK9hgGEBw4JpsQAGWCw7n9vxc8Ruw53k09csVrBYMHf8/JnnsFiIupw442bHc7b2uS22jsdWzI57rFbifg/2cBLTagOrDdPSfo/V1v6c5dOfDZuNZDwLoyUGVjtY7Zg2B9js7fPJXuXYQYNZ1ljPh7VV3LFmGffOmkMg1aFERERERERkr5NMJkkk4hgGhMNhYrEoX3f1sq6MTVdWqw2LZc/XR6qwFDBNjKYaiEcxPvO08aVvABPoSh2YBOydHBsHnF2YOwp4d/O8aVjA1lFgWu1gcxBxuXAbNkybE+xOTLsDbE5Me8djmwPT7sKwO4jnZGKNGJgOF6bDAw4XpsON6XCDw91exkpaMQyDS8dOZGNrM5XhEHevW8UfCg5KdSwRERERERHZS5imSXNzPaFQ687n6ustJJPJTr2/K2PTldvtIyMjB8P4qmbpq6mwFDAMQmffQq49Sn1dM2Yy2V5iJpNg7riZ0PHYIEmW30VTY2v72GQCkgkM0+z4OQlmx30yiWEm8HnstDW3YSYSGB3j229xjESifXzHay67QTgYhkS84/V4+8+JWPvPyQQkdjwfw2ImMKOR9sI1mfj0sMwkxCIYscjO57pStMYB91e8btocO8tL0+km6vPjsrhIurzg8mG6vJhOb/u9y4vpbH8Otxcz6enOfynpBK/NzhUTpnL50vl8XFvNMxs3cnhWQapjiYiIiIiIyF5gR1np82XjcDgxDAOr1SCR6NySya6MTTemaRKNRmhtbQAgMzO323OpsJR2FitGdj5mwr1z2fGX/fYwDLAE/CRqWzq1RNkwwBbwE+vEeMMAf8BPUxfmDgT81O4Yn0xCIgbxGEYi2n4fj7WXnfEomV4rzXWNEI1gxCMQi7YXmrEdjyMYsShGPIyDBLG2VoiGMCIhiIUxoqH2ohQw4lGMeBSC7ddKTFZ1/jdUxDDwuP2Y7gxMTyamJ+Mzt8zP3PsxvYPbC+OvXPMqnzXGn8kPRo7lzxvXcc+KFQyd4WakNyPVsURERERERGQASyYTO8tKn+/Tv4PabBbi8c6tmuzK2HTkcLSfM9va2oDfn93t08NVWMrAYrGApeP07o6ndtwbBlgDfhLZnStOfQE/zbsrTuMxjGgIomGMWAgjGsaIBslwJGmpqcMItWKE2zAibRBuwwi3YkQ67sNt7QWpaWIJNkOwGerKvjJLBPDanZi+HJL+nM/c5+58bPpzMD0Z7dcOFQBOKBrK8sZ65tXVcNfaldw9Y3/sOo1fREREREREekki0X7W547Sbm+14/gTiTgWi6Nbc6iwFOkqmx3TZgdPxhfK0HhnVpEm4+S6TRrKyiHYjBFsxgg2ddy3/2wJNX/6OBrCiEUwGiqwNFR86bymxYrpyyaSW4jTGyCZVbDLDZev576DfqD9epaTWLf4Y7a2tfLPLRs4b+TYVMcSERERERGRAW5Prt04EPTE8auwFOlrVhtGhp9kvr1TKz1zM+zUby3FaK7DaK3H0lKP0Vr/6c8t9RhtjRjJBEZzLWZz7W43ODJdXpKZnxaYZnYByeEjwZ4LzoFZZmY6HFy1zz786uOPebZsC7Nz85icmZ3qWCIiIiIiIiLyFVRYiqQ5w+HCzC4kmVX45YMScYy2RiytdWQmW2kt3YKlsQqjsQpLYxWWtkaMcBvW8CasVZt2vi0K+ICkL5tkYCjJwGASgSHtP+cWg73/L2M/tKiIuYVFvFVZzh/WreS+febgsemPPhEREREREZF0pb+1iwwEVhtmRoBkZgBrwE9syOdOTY+GsTRV71piNlZia6qExhosrQ1YWhtgy7KdbzExMLMKSOQNIZk7hGT+UJKWKWB66G8bAF0wajzLGuqpCof4y6Z1XDJ2UqojiYiIiIiIiKSNhoZ67rjjFpYsWYTVauXoo7/JRRf9FFuKFvyosBTZGzhcJPOGkswbuvOpnTusb6/CqC3DWluKpaYUS20pltptWEItGI2VWBoroWQBANEXwePJJDFoNMmiMSQGjSFROAocrlQdWad4bDZ+Pm4yVy9fyBuV29kvN599c/NSHUtEREREREQkLVx33VXk5eXz/POvU1dXy5VX/oJ//etxvvvdc1KSR4WlyN7O6SFZNJZk0Wc2pDHN9s1/akt33qw1W7HWbMMSbMKycRFsXNQ+1DBIBoaS2FFgFo2BnK84fT1FpmTlcFLxMJ7fvpV716/igVkHkGnv3m5lIiIiIiIiIp1mmhCNdW5s0gLx5J5/pt3WvlKpE8rKSlmyZBHPP/8aLpeL4uLBnHvu+Tz44L0qLEUkjRgGpjeLhDeLxLApO54iN9NBw+oVWMo3YC0vwVpRgqWlrqPM3ArL3gbaN/iJjpiEvWgisWFTMbMHdfoPyt50zojRLG6oZVuwjQdKVnPVhGl7/e5tIiIiIiIi0otME8ejz+Euq+zTj40PLiT0vW916u/imzdvJCMjk0Dg0zMRhw8fSVVVJS0tLfj9/l5MunsqLEWk0wy7k2TxOBJF49jxb0NGSz3WihKs5SVYKkqwVm3CCLeRXDMf55r5OIFkRh7xEdNIDJ9KfOhkcHpSkt9hsfKLcVP45dJP+Ki2mnerKzi8oCglWURERERERGQvkeYLZYLBIC7Xrpd62/E4FAqqsBSR/sf05xD370d87H7tTyTiWGu3klG7gfCq+VjL1mJprsGx7G1Y9jamYWk/fXz4NOLDp2EOGtGneUf7M/jO0FH8c+sG/rhhLZMzc8hzpfc1OEVERERERKSfMgyi3z+ZeCjaqeE2m4V4H58S7nK5iUTCuzwXDrc/9ni8e56lG1RYikjPstpIFo7CNnk64cnfwIyEsZauwrZlObYty7A0VGLbvg7b9nU4P/wXpttPdNw+WIfNJD5iOth6/7qSpw0dzoL6Gta1NHH3+pXcNGUmljT/Fy8RERERERHppwwDHPbOjbVZwNIDhWUXjBw5iqamJurr68jJyQVgy5ZN5OcX4PP5+jTLDiosRaR3OVwkRs0kMWomEcBorMa2ZRnWLcuxbVuJEWohufQ93Evfw3S4iY/dj9iEA0kMmQQWS69EshoWfjFuMpcu/phljfW8Ul7KCcVDv/6NIiIiIiIiIgPMkCFDmTp1OvfccxdXXHENjY2NPProIxx33Ikpy6TCUkT6lJmVT2z6XGLT57afPl5Zgr9sObEl72Fprce+8l3sK98l6ckkPn4OsQkHkiwc3ePX/Cj2eDlv5Fj+uGEtf9u8nunZuQz1pmapu4iIiIiIiEgq3Xzzb/n97+/gtNNOxDAsHHPMcZx77vkpy6PCUkRSx2ojOXgC9un70rTfaVhK12Jb+xH2dfOwBJtwLH4dx+LXSWbmE5twIPHxB2LmDe6xjz9u0BA+qa1hSWMdv1+3grtm7Ntjc4uIiIiIiIj0Fzk5udx8829THWOn3jnfUkSkqwwLiSETicw9n9YL/0jw5MuJTTgQ0+bE0lSNc95zeB+9DPejVxD/4AWIBPf8Iw2Dn46bhNdmo6Slmae2bu6BAxERERERERGRPaEVliKSfqy2nde9JBrGtnER9rUfYd28FGvNVuIv/Rmv4x/EphxOdMYxmFn53f6ogNPFhaMn8Lu1K3hi6ybmjhpGnv5oFBEREREREUkZ/a1cRNKbw0V8woHEJxwIoRbs6z7GvexNqCnDsehV7ItfIz56NrGZx5IoHt+ta10emlfIJ3XVvF9TxW8WLuT30/bFZmgBuoiIiIiIiEgq6G/kItJ/uP3EZxyN4xcPETrlCuLDp2KYJvaS+XievBHPP6/BtvoDSMS7NK1hGPx49AQy7XY2NTfz1LZNvXQAIiIiIiIiIvJ1VFiKSL9jWCwkRs4gdOrVtJ17J9GpR2Da7FirNuF+9X68D1+CY95zEGzu9JyZdgcXjpkAwL+2bWZza0tvxRcRERERERGRr6DCUkT6tWRgCJGjL6DtggeIHHQGSW82ltYGnB88hfdPFxF79n6MlrpOzXVQoIDDiopImCZ3r19Jwkz2cnoRERERERER+TwVliIyIJieDKL7n0zbBfcROvYnJPKHY8RjJD55Dc8jP8fx/pNfu7O4YRhcPn06PpuNja0tPFu6tY/Si4iIiIiIiMgOKixFZGCx2ohPOoTg2bcRPPM6jOGTMOJRnJ88j/eRn2Jf/PpXXuMy4HZzwajxADy+dSOlwba+Si4iIiIiIiIiqLAUkYHKMEgOmYjjx78l9K3LSOQUYQm14HrnUbx/+yW2tR+Dae72rUcUDGJmdi4xM8k961eS+JJxIiIiIiIiItLzVFiKyIBmGAaJMbMInnsn4bnnk/RkYmmswv3yPXgeuxZr6ZrdvueiMRNxW62sbW7i5fJtKUguIiIiIiIisndSYSkieweLldi0o2g7/x4iB5yGaXdirdyI56kbcT93J5basl2G57vc/GDEWAD+vrmEytBXX/9SREREREREpL9raGjgjDO+xeLFC1OaQ4WliOxdHC6iB5xC2/n3EJ02F9OwYNu4CM//uxznG3/GbGnYOfQbgwYzJTObSDLJvSWrMXVquIiIiIiIiAxQy5cv5cc/Po/t28u+fnAvU2EpInsl05tFZO4PaTvvd8TGzMYwTezL3yHy+59gW/sRmCYWw+DSsZNwWiwsb6znjcrtqY4tIiIiIiIi/Y1pQjTct7cuLrh57bWXufHGa7nggp/00pfQNbZUBxARSSUzp4jwSb8ktn0dzrf/irVmK66X7sU6Zh6Ro37IIG8m5wwfw8Ob1vGXTeuYmR0gz+VKdWwRERERERHpD0wTxz+vx719XZ9+bLx4HKEzbwDD6NT4fffdn7lzj8Fms3H99Vf3brhO0ApLEREgUTyO0Nm3YD3qu5gWK/aS+XgevQzb2o85vngo4zMyCSUSPLBBp4aLiIiIiIhIF3SuM0yp3NwANlv6rGtMnyQiIqlmtWGfexYtRVNwvvYQ1pptuF++B9v6T/jpgd/hktXLWVhfy7vVFRxRWJTqtCIiIiIiIpLuDIPoWb8hHgp1arjNZiEeT+7559qdnV5dmY5UWIqIfE6yYATB792K4+NncXzyPPb18xhfuprvzTmLR1sj/HnjOmbk5BLAn+qoIiIiIiIiku4MAxydvLSYzQKWHigs+zmdEi4isjtWG9GDTif4vVtIBIZiCTVz9n//yOhEiJZ4jIdK1qY6oYiIiIiIiMiApMJSROQrtK+2vIXI/idjxeDKNW9gNZN8WFvFf8rKUh1PREREREREZMBRYSki8nVsdqIHnUHwrJsY6fZwVvlyAO6c9yEtbS0pDiciIiIiIiLScz74YCH77DMrpRlUWIqIdFKycBTBs2/j9OKhDA810GDYePS//8ZorEp1NBEREREREZEBQ4WliEhX2OxwyJlcOno8hmnyur+INc/fg3XLslQnExERERERERkQVFiKiHTDuNHTOGVIMQC/L9oH47k7ccx/EUwzxclERERERERE+jcVliIi3fSTfWYRcDgpd2XwaNF0nP97HNfL90I0nOpoIiIiIiIiIv2WCksRkW7y2u1cNHYiAP8aNJV1vjzs6z7G88R1uq6liIiIiIiISDepsBQR2QP75uZxSF4hSeD26d8m6snCWrMN7z+vwbplearjiYiIiIiIiPQ7KixFRPbQBaPG4bfZ2RSN8djRl5AoHIURbsX979uwz39J17UUERERERER6QIVliIieyjL4eT8kWMBeLyygpKTLic6+TAM08T1v8dwvnwfpq5rKSIiIiIiItIpKixFRHrAEQVFTM/KIZpMcv+mEsJHX0D4yB9gWqzY135E9MHLMJprUx1TREREREREJO2psBQR6QGGYXDxmIk4LRZWNDXwVlU5sRlHEzr91yQ9mZgVm3E/fj1GfXmqo4qIiIiIiIikNRWWIiI9pNDt4azhowH4y+b11EciJAaPJ3T2LRh5g7G01OF54gYsVZtTnFRERERERETkUyUl6/nZz37CsccewYknfoObbrqOxsbGlOVRYSki0oNOKh7KaF8GbfE4f9q4FgAzI4Djx3eQKBiBJdSM56nfYC1bm+KkIiIiIiIi0hdM08SMhfr21oXNXyORMJdddilTpkzjxRff4B//eIrm5iZuvfXGXvxWvpotZZ8saacrv5hFZPeshoVLx07kZ4s/4cPaKj6ureaAvHwMXyahM36N69k7sZWtwf3MrYRO/DmJkTNSHVlERERERER6iWmaNL50MfHqlX36udb8yfiPvQ/DML52bFVVJaNHj+Xcc8/HarWSmZnFSSd9m5tuuq4Pku6eCkvBNE1uWF7CkvomnFYLTosFp9WKy2rBZbG0P9fxvMtqxWm1kF3uwogmcFutuK1WPDYLHqsVj23H4/Z7l9WCtRO/OUQGkpG+DL49ZDjPlG7moQ1rmJadTQDA6SF0ylW4X/oDtk1LcD//O8LfvIj4+ANSHVlERERERER6S5r3IkOHDueuu+7d5bn//vc/jBs3IUWJVFhKh9Z4nCQQSiQJJZIQi/fIvAbgtlrJcNrwWKz4bFb8dhs+mw2/3YrfZsNnt3XcW8mw2zC9DhLJJFZDVyyQ/us7Q0fyYU0VFeEgj24q4frC/dpfsDsInfRLXK89iH3tR7hevo9IJEhs2lGpDSwiIiIiIiI9zjAMso6/n3g42KnxNpuFeDy55x9sc3VqdeXnmabJww8/xIcfvs8DDzy85zm6SYWlYBgGd+wzAZvfSXlNM6F4kkgiSSSZJJxIEEkkCSfbnwsnkkSSCQyHjfrWEMF4gmAiSSieIJRIdDxuv08CJrQ/Dia6nMtvs5HtsJPlsJHlsHf8bCfLbt/5fI7TTrZOZZc05LRauWTsRK5evpBXK8o4sWYkQwxX+4tWG+HjLsZ0enEsewvXW49ghNuI7ndSakOLiIiIiIhIjzMMA8Pu7txYmwXD6IHCshva2lq59dYbWbduLQ888DCjRo1OSQ5QYSkdrIZBrsuJ6Xbxdf2fYUAg4Ke2tuVLx5qmSTRpEky0F5l2n5PSmmaaY3FaY3Fa4nFaYwla4nFaYnFa43FaYomO+/bVni3x9nHbvuYfIWyGQa7TQZ7TQZ7rs/fOnY/dNmu3vheRPTE1K4ejC4t5s3I7ty5ezD0z9sNudPxaNCxEjvoBpsuL85Pncb7/BETaiB78nbQ/XUBEREREREQGlu3by7jsskspKCjkkUf+QVZWVkrzqLCUXmEYBk6rgdNqIcewE8jxk580OlWGZuf62FzRQEMkRmM0RmMsTkO04+fors81RWPETZOqcISqcASadj+vz2Ylz+VgaIaXPJuNIreLYo+LYreLDLutW8ukRTrjByPGsqC+hm2trTyxdRPnDB/z6YuGQfTgMzFdXlzvPYZz/osYkSDRo36QusAiIiIiIiKyV2lububSS3/MzJmzufLKX2OxpP4SfSosJe1YDYMsh51Mu/1rxyYxMbwO1lXUUx2KUhOJUh2OUhOJUBNuf9wWT9AaT9DaGmJza+gLc3htVga7XRR5Pi0xi70ufFme3jg82cv47HYuHD2BW1cv49+lWzgoUMhIn3+XMbHZJ4DTi/Oth3Esexsj0oZ59hUpSiwiIiIiIiJ7k1dffZGqqkreeect/vvft3d57a233k9JJhWW0q9ZDYOAx4Ul08/4jN2PCcYT1ETaS8wWC6yraWJ7MEx5MLyz0FzX0sa6lrZd3mcsgGKPi5E+zy63TMfXF6kin3VgXgGHFRXxbnk5965fxV0z9v3CplKxqUdgOj24XrkP+9qPif3zVjj2UrDoj2kRERERERHpPWee+T3OPPN7qY6xC/1NWAY8j83KMJub4T53+7U3s7N2npoeSSSpCIXZHgyz/XP3rfEEZcEwZcEw/6uu3zlfwGlnpM+7s8Ac5feQ73Kk6Oikv7h8+nQWVFezobWZ58u2csqQEV8YEx+3PyGHG/cLvyO5Zj5O8wHC37wE0mA5voiIiIiIiEhfUWEpezWn1cJwn4fhvl1P/zYMMHwOFm6tYWNLGxtbg2xqDVIRilAbiVEbaWR+XePO8T6blSmBLMZ4XEzM8DM2w4tdJZN8RsDt5vyR47hn/Soe27qROYECitxfvOxAYsQ0wt/6Je7nfod97cdgcxL+xgVg6NeTiIiIiIiI7B1UWIp8iVyXk5m5meyTk7nzuWA8weaO8nLHbVtbiNZ4go8r6/i4Y5zdMBib4WNylo+JmX7GZ/jwaKfyvd7cwiLera5gWWM9961fxa1TZ+12w6fEiOnYv/Mroo/dhn3lu5h2F5Ejvq/dw0VERERERGSvoMJSpAs8NiuTsvxMyvp005RYMsm2YIit8Rjzt9eyqrGFxlicVU0trGpqASqwACP9HiZltr/3UL8rZccgqWMYBpeMmchFiz5iRVMDb1Ru55hBg3c71jrlQCLHXojr1QdxLHkd0+EievCZfZxYREREREREpO+psBTZQ3aLhdF+L/sH/ByZnUUyaVIeirCysYXVTS2samqlKhxhQ0uQDS1BXiir4raVGxif4WN2biazc7MY5nXvdqWdDDyFbg9nDx/DI5vW8ddN65mVEyDg3H2BHZ90COFoGNfbf8X5yfPgcBPd76S+DSwiIiIiIiLSx1RYivQwwzAo9rgo9rj4RlEeALXh6M4Vl6saW9gWDLOmuZU1za38ffN28pwOZudmMTs3k6lZGTisul7hQHZC8VD+V1PB+pZmHtqwhmsnTv/Swjo2/WiIRnD97zGc7z+BaXcS2+eYPk4sIiIiIiIi0ndUWIr0gYDLwaGuXA4tyMUwIOmx80bJdubXNbKsoZmaSJRXy6t5tbwap8XC9OyM9gIzkEkg1eGlx1kNg0vHTuJni+fxSV0NH9RWcXBe4ZeOj+17AkY0hHPes7jeeRTT7iI+5bC+CywiIiIiIiLSh1RYiqRAvsfFscX5HFOUTziRYEVjCwvqGllQ10htJMYndY18UtcI62F8tp85OZkcmp9LrtOR6ujSQ4Z7/Zw2ZARPbNvEnzasZVpWDhn2L//vGz3wNIxYCMei13C9+SfCDhfxcfv3YWIRERERERGRvqHCUiTFXFZrx+ngWZimyebW0M7ycn1LG2sbWljb0MKjG8uYlp3B4QW5zAlk49au4/3e6UNH8mFtFduCbTyycR2/GD/lywcbBpHDzoFoBMeKd3C9ch8hm4Pk6H36LrCIiIiIiIhIH1BhKZJGDMNgpN/DSL+HM4YX0RiLsSoc4sUN21nd1MrShmaWNjTzoGUrcwJZHFYQYHp2BlaLNuzpj+wWC5eMncSvls7nneoKDs0fxMycr7gIgGEQmXs+RjyCfc2HuF/8A+FTroDAnL4LLSIiIiIiIgPOokUL+OMf72fr1i24XC4OP/xIfvKTS3F+ySaxvU07e4iksWyHnW+NLOaOfSbw8H5TOWt4MUVuJ5Fkkner67lhxXrOm7eMRzZsY2NLG6ZppjqydNGEjCxOKB4KwAMlqwkl4l/9BouF8DEXEhs9CyMRw/XcnSS3rumDpCIiIiIiIjIQNTQ0cPnlP+Pkk0/l9df/y1//+hhLlizin//8fynLpBWWIv1EodvJmcOLOGPYINa3tPHfyjr+V11PQzTGC2VVvFBWxVCvm2+NLuYAvx+vTb+9+4uzh49mXm011ZEwf9+8gR+PGf/Vb7DaCB9/KcZzd2LbuoLoX6/H8p0bSOQO6ZvAIiIiIiIi0mmmaZKMhzo1NoGFZDy5x59pWF0YRufOxszOzubll9/E4/FimibNzY1Eo1GysrL2OEd3qdEQ6WcMw2Bcho9xGT5+OHoIi+ub+G9VHfNrG9nWFuLeZRv4s9XCkQUBThhcQLEnNcu3pfPcVhsXj5nIdSsX83L5Ng7NL+TggP+r32RzEDrpl3ieuRVr+Xpc//4twe/ehOnL7pvQIiIiIiIi8rVM02TDfy4mWLuyTz/XkzuZ4Yfd1+nS0uPxAvDtbx9HTU0106bN4JvfPLE3I34lnRIu0o/ZLRb2C2Rz5aTR/P2A6Vw0dhgjM7yEE0leKa/mx/NXcOPy9Syub9Lp4mlun5wARxYUYQL3rF9FNJH4+jc5XIS+fTlGoBhLcy3u5+6AaLjXs4qIiIiIiEhX9J99J5588lmef/41LBYL1157RcpyaIWlyADhs9s4tjif700dyX9KynmhrJKFdU0srG+/DfG4OGFwAYcX5OKyaofxdHT+yHEsqq+lNNjG39au5ZSCoV//Jrcf+w9uJHz/L7BWbcb90j2ETr4MLPpvLCIiIiIikmqGYTD6yPuJRYKdGm+1WUj08Snhn+V0unA6XVx44SVccMG5NDc3k5GRscd5ukorLEUGGMMwmJ6TwXVTxvLHfadwQnE+bquF0mCYB9dv5byPl/HoxlJqwpFUR5XP8dvt/Hh0+/Ur/9+6dWxoae7U+yy5gwiffDmmzYFt8xKc//kbaEWtiIiIiIhIWjAMA4vN3ambtZPjvu7WlbJyxYplfPe7pxCLxXY+F4vFsNvtuN3u3vhKvpYKS5EBrMjj4oIxw/jbnGmcP2oIBS4nrfEE/y6t5IfzlnPNxyvZ0tq5f+WRvnFgoIADAwUkTJM/rFtJLNm5f1lLFo0hfNwlmBg4lr2NY/6LvZxUREREREREBoJRo8YQDof54x/vIxaLUVlZwf33381xx52E3W5PSSYVliJ7Aa/NxklDCvnTflO4ZvJopmb5SZrwn7JqLlmwijtWbaS0rXM7lknvMgyDi8ZMINvpZEtbK09u3djp98bHzCZyxDkAON9/Atvaj3orpoiIiIiIiAwQHo+Hu+66j02bNnLCCUdz8cUXMHv2flx66S9SlknXsBTZi1gNg/0D2ewfyGZzW5DnK6p5p6yG92vq+bCmnkMKcvnOsCKKtLN4SmU6HPxqxgyumjePp0u3sF8gn7H+zE69N7bPsViaanAsehXXaw8S8maTGDKhlxOLiIiIiIhIfzZixEj+8IcHUh1jJ62wFNlLjfR5uHXOFO6dPYn9crNIAu9W1XHh/BXcu3YzVSFd4zKVjigu5pC8QpKY3L1uJdFkJ3YN7xA57HvExuyLkYjjfuF3WOq292JSERERERERkZ6lwlJkLzfS5+HaKWP4w8yJzMrJJAm8VVnLj+av4IF1W7Q5TwpdOGY8WXYH24JtPN6FU8MxLIS/eTGJQWMwwm24n/0tRltjr+UUERERERER6UkqLEUEgNF+L9dPHcudMyYwPTuDhGnyekUNF3yygj+VbKU+Ek11xL1Oht3BRWMmAvBs6RbWNTd2/s12B6GTLyeZVYClqRr3c3dCNNw7QUVERERERER6kApLEdnF+EwfN00bx23TxzM500/cNHl5ezXnz1vOQys2Eop3/tRk2XNzAvkclj+IJPCHdauIJDr//ZueDIKnXEnS7cdauRHXK/dBJ3cdFxEREREREUkVFZYisluTs/zcOn0cN08bx/gML9Gkyf9bu5UffbKCd6vqME0z1RH3Gj8aNZ5sh4OyUBuPbd3Qpfea2YMIfesyTKsd+8ZFON75f/pvJyIiIiIiImlNhaWIfCnDMJiWncEdMyZw7eTRFHtd1Edj3LVmE1csWcuGlrZUR9wr+O12Lu44Nfy5sq2saWrs0vuTxeMIf/MiTAwcS94g8cELvZBSREREREREpGeosBSRr2UYBvvnZfP4N/bjnJHFOC0W1jS38otFq7lv3WYao7FURxzw9svN58iCIkzgD+tXEu7CqeEA8XH7Ezn0rPafX/kL1k1LeiGliIiIiIiIyJ5TYSkinea0Wjl9WBF/2m8Kh+XnYAJvVtTy409W8EJpJXFdH7FX/d+oceQ4nJSHgvxjS0mX3x+bdRyxqUeAmcT10r1Yast6IaWIiIiIiIjInlFhKSJdlut08MuJo/jtjPGM8nloSyR4ZGMply5cxZL6plTHG7B8NjuXjm0/NfzF7dtY2VjftQkMg8hRP8AYOQUjGsL93B0YweZeSCoiIiIiIiLSff2qsFy7di3nnXce++67LwceeCC/+tWvqK9v/wv7smXLOO2005gxYwZHHHEETz/99C7vfe6555g7dy7Tp0/n29/+NkuW6HRIkT01MdPPXTMncvHY4WTYbZQGw1y3fD03ryhhe2so1fEGpFk5ecwtKMYE7l6/inAi3rUJrDYc37uKZGY+lqZqXC/+Abo6h4iIiIiIiEgv6jeFZTgc5vzzz2fGjBl88MEHvPzyyzQ2NnL11VfT1NTEBRdcwLe+9S0WLFjALbfcwm233cby5csB+OSTT7jpppu4/fbbWbBgASeeeCIXXnghoZAKFZE9ZTUMvlGUx5/2ncKJxQVYgHm1jXz3jU/497YKEkntSN3Tzh81loDDSWU4xN82df3UcMObSfjbl2M63NjK1uB8+6+gncNFRERERET2eolEgosvvoBbbrkhpTn6TWFZXl7O+PHjueiii3A4HGRnZ3PGGWewYMEC3nzzTbKysjjrrLOw2WzMmTOHE044gcceewyAp59+muOOO46ZM2dit9s599xzyc7O5tVXX03xUYkMHD67jf8bM5T7Zk9mapafSDLJ3zaWcdni1WxuDaY63oDitdm5dOwkAF4uL2VhdXWX50gGhhA6/lJMw8Cx4h3si1/r6ZgiIiIiIiLSz/ztbw+zfPnSVMfAluoAnTVy5EgeeeSRXZ574403mDRpEiUlJYwdO3aX10aPHs0zzzwDwIYNGzjllFO+8PratWu7nMMwuvyWfmHHcXXm+Loytr/OrRzdHz/M5+bWGeOY19LKH5aWsKE1yM8XrebUoYWcObwIu2XXfyfpj8eYDjlm5gY4ZtBgXq8o4+ZFi7hvn/1xW7/+j/TPzp0cNYPoYd/D+d9/4Hz3H5g5g0iMnNGt3AP5u063udMlR2/OrRx9N3e65OjNudMlR2/OrRx9N3e65OjNuZWj7+ZOlxy9Obdy9N3c6ZKjN+dOlxxfNf7L3m+aJol4587yNbCQiO/5hrYWqwujswfUYdGiBbz77jsceugRe/z50P59dPY7+sJ7TbP/nQdomiZ33303jz/+OP/85z/5+9//TiwW44477tg55umnn+bPf/4zb731FnPnzuVHP/oRp5566s7XL7/8chwOB7fccksqDkFkr1AbivC7Jet5d3sNAMP9Hq6ZPYEpuZkpTjYwtMVinPX221QEg3xz6FCunz27y3OYpkn83/eSWPAmOD04LroLS8HQXkgrIiIiIiIysIXDYTZu3EQgUIjD4QTa/8614P2f0FS/sk+zZOVMYdbBD3S6tKyvr+eHPzyHO+74PU880X7G8nXX3ditz45GI9TWVjJq1EhcLle35ug3Kyx3aG1t5aqrrmLVqlX885//ZNy4cbjdblpaWnYZFw6H8Xq9ALjdbsLh8Bdez87O7vLn19W1DMhLvRkG5Ob6O3V8XRnbX+dWjp6Z2whGuWzscOZkZfBQyVa2tAS54J1FnDC4gLNHFOO2Wfv9MaY6x8/HTuLKpQt4dds2JnkyOaxgUNfnPvgc3BWlWMvWEP7L9QS/dzN4MtLmGPtjjt6cO11y9ObcytF3c6dLjt6cO11y9ObcytF3c6dLjt6cWzn6bu50ydGbcytH382dLjl6c+50yfFV42OxKMlkkkTCJN6xStI0TQy6ttKxJ5gmxOPJThWWyWSS66+/hjPO+C4jRoxmx9rGeDdXeiYSJslkkoaGNuz22C6v7fjuvk6/Kiy3bdvG//3f/1FUVMQzzzxDTk4OAGPHjuXDDz/cZeyGDRsYM2YMAGPGjKGkpOQLrx9yyCFdzmCaA3tviq4cX1e/i/44t3L0zNwH5OUwJSuDv2ws5T+VtbxYVsW8mgYuGjecmR2rLfv7MaYqx6TMbM6bMIG/rFnDAyVrGOfPpNDt6drcFhuhE3+O57Fr23cOf+EPhE67Bmy2LuceyN91us2dLjl6c27l6Lu50yVHb86dLjl6c27l6Lu50yVHb86tHH03d7rk6M25laPv5k6XHL05d7rk2N343b3XMAxmHfwA0Ujn9naw2SzdLgo/qyunhP/jH3/D4XBw6qln7vHnflZXv8/P6jeb7jQ1NfH973+fffbZh7/85S87y0qAuXPnUltby6OPPkosFmPevHm89NJLO69beeqpp/LSSy8xb948YrEYjz76KHV1dcydOzdVhyOy1/Hbbfxs/AhunDqWfKeD6kiU65ev5+41m2mOxr5+AvlSPxg/nvEZmQQTce5at5KE2fX/uZmeDEIna+dwERERERGRnmYYBlabu09vXbl+5RtvvMqSJYs55pjDOOaYw3jrrdd5663XOeaYw3rvS/ka/WaF5bPPPkt5eTmvvfYar7/++i6vLVmyhL/+9a/ccsst3HvvveTk5HDttdey//77AzBnzhyuv/56brjhBqqqqhg9ejQPP/wwWVlZKTgSkb3bPjmZ3D97Mv/YXMbL26t5u7KWJW98wk/GDGPf3KxUx+uXbBYLl4+fwsWLPmZNcyNPbt3EWcNHd3meHTuHu5+7A8eKdzADg+GYM3ohsYiIiIiIiKSLxx//9y6Pb7nlBgCuueaGvg/Tod8Ulueddx7nnXfel74+ZcoUnnzyyS99/aSTTuKkk07qjWgi0kVum5ULxgzj4Pwc7l23hbJgmJtWlPCNQXn8cNQQ3DZrqiP2O4VuDxeNmcjv1q7gqW2bmJ6dy6TMrl+nNzFyBpFDv4fr3X/gePcfJIaPgsC4XkgsIiIiIiIisnv95pRwERl4JmT6uXfWJM4aOxQDeKOihksXrmJtU2uqo/VLh+UP4vD8QSSBu9auoDXevVPtYzO/SXTyYRimSezx2zFqy3o2qIiIiIiIiKSta665IaWrK0GFpYikmMNq4ZJpo7ll+jjynA4qwxGuWLKGf24uI57c8wsN720uHD2BQpeb6kiYB0rWYHbnOpSGQWTu+SQGj4dICPdzd0KopefDioiIiIiIiOyGCksRSQtTszO4b/YkDi/IJQk8tbWCyxevobQtlOpo/YrHZuPy8VOwYPB+TSXvVJV3byKrjdBJv4DsfCyNVbhfuhsS8R7NKiIiIiIiIrI7KixFJG14bTZ+MWEkV0wchd9mZUNrkJ8tWsVLZVUktWN1p43LyOKs4aMAeGjDWspDwe5N5MnA8f3rMe0ubNtW4fzv33swpYiIiIiIiMjuqbAUkbRzUH4O982ezD7ZGUSTJn/esI3rl6+nLhJNdbR+49QhI5icmU04meDOtcu7fXq9ZdBwwsdfjImBY+mb2Je82cNJRURERERERHalwlJE0lKu08ENU8fy4zHDcFgsLG1o5uIFK/lfVV2qo/ULVsPgl+Om4LXZKGlp5rGtG7s9V2L0LKIHnwmA851HsW5b2VMxRURERERERL5AhaWIpC3DMDiuOJ97Zk1kjN9LazzBHas3cf0nq2iL63qKXyfP5eKSMRMBeKZ0M8sb67s9V3TfE4lNOAjDTOJ+8Q8YDZU9FVNERERERERkFyosRSTtDfa4uWPGeL4zrAiLAW9sq+LSBatY29Sa6mhp76C8Qo4uLMYE7lq7gpZYrHsTGQbhb1xAonAURritfefwSDevjSkiIiIiIiLyFVRYiki/YLNY+O6IYn47YwKDPC6qwlGuWLKGJ7eUk9CGPF/p/0aNo9jtoS4a4d71qzC7+33ZHIS+dRlJXw7W+u24X74XunltTBEREREREZEvo8JSRPqVCZk+/nH0vhxWkEMSeGzLdq5ZupbqcCTV0dKW22rj8vFTsRkGH9VW89zmzd2ey/RlE/rWZZg2B7bNS3H+77EeTCoiIiIiIiKiwlJE+iGf3cZlE0fxi/EjcFstrGpq5dIFq3i/uvvXaBzoRvszOGf4GAB+v2wZa5sbuz1XsnAk4WMuBMCx8BVsK9/tgYQiIiIiIiIi7VRYiki/dXhhgHtnTWJchpe2RII7Vm/knrWbCcUTqY6Wlk4ePIw5gXxiySS3rlpGQ7T7q1Lj4+cQmXMKAK63HsFStq6nYoqIiIiIiEgf+89/3uTQQ/dj7tyDd95uuunXKctjS9kni4j0gEK3i9unj+fJrRU8vbWctytrWd3UwmUTRjE205vqeGnFMAx+MW4yl0cWsKWlhTvWLOfmqTOxGt37t6voAadgqS3FXjIf1wu/xxx2N+Du0cwiIiIiIiL9nWmaxBOhzo3FQiKx53sFWC0uDMPo9Pg1a1bzjW98k6uvvn6PP7snqLAUkX7PZrHwvRHFTM/O4K41mygPRbh8yRrOHlHMBbm+VMdLKx6bjdv335/z3nmHFU0NPLq5hB+OHNe9yQwL4WN/gqWpGmv1FqL/7zdw+vVgd/VsaBERERERkX7KNE1en3cRNY0r+/Rz87KmcNSs+zpdWq5du5rDDz+ql1N1nk4JF5EBY3KWn/tmTeLAvGwSpsmjm8q45L0l1IajqY6WVkZkZPDzcZMBeK5sK/+rruz+ZA5X+87hnkzMis24Xr5PO4eLiIiIiIh8VhdWOqZCMplk3bq1fPzxB5xyyvGcfPI3+e1vb6G5uTllmbTCUkQGFJ/dxhUTR/Gfylr+tGEbi2oaubihhYvHDufA/JxUx0sbB+YVcOqQETxTupl71q9kqNfLcK+/W3OZGQHCJ1+G56mbsG1chPO9fxA5/Ps9nFhERERERKT/MQyDY/Z7gEg02KnxVmvfnxLe2NjA2LHjOOywI7n55jtobGzklluu56abfs2dd96zx1m6QyssRWTAMQyDowblcc+sSUzI9tMaT3D76o3cvXYzQW3Is9PZw0czPSuHSDLJrauX0RqPdXuuZNEY7Gf8AgDHotewL3mzp2KKiIiIiIj0a4ZhYLO6O3Wz2zo37utuXbl+ZU5OLg888DDHH38SLpeLwsJCfvKTS5k37yOCwbZe/Ga+nApLERmwij0uHj5iJmcMG4QB/Keylp8uXMXaptZUR0sLVsPg8glTyXO6KA8F+f3alSRNs/vzTT2YyMFnAuB8529YNy3pqagiIiIiIiLSSzZsKOGhh+7D/MzfB6PRGBaLBZvNnpJMKixFZECzWSycPXIwt00fT77TQWU4whVL1vDElu0kkt0v5waKTLuDayZOx25YmF9fw1PbNu3RfLH9TiI6+TAM08T90j1Yarb2UFIRERERERHpDRkZGTz77L94/PG/E4/Hqays5MEH7+HYY4/H4XCkJJMKSxHZK0zK8nPv7Ekclp9DEnh8SzlXLl1DZSic6mgpN9qfwU/GTADg8a0bWVhf0/3JDIPI3POJD5mEEQvjfvYOjNaGHkoqIiIiIiIiPS0/v4A77rib999/j29+80jOP/9sJkyYyM9//quUZVJhKSJ7Da/Nxi8njuKXE0bisVpZ29zGpQtX8Z/K2l2Wvu+N5hYWc+ygwZjAnWtXUBHq3AWhd8tqI3TSz0nkFGFpqcP93J0QVTEsIiIiIiKSrmbMmMkf//hX3nzzPV5++S1+9rPLcTqdKcujwlJE9jqHFeRy3+xJTMr0EUokuXvtZn67eiMtsXiqo6XUBaPGM86fSVs8zq2rlxFO7MEGRS4foW9fQdLtx1q1CderD0Byz3e6ExERERERkYFPhaWI7JXyXU5umT6es0cUYzUMPqxp4OIFK5lfVZ/qaCljt1i4auI0suwONre1cN/61Xu08tTMKiD0rcswrXbsGxbg/N9jPZhWREREREREBioVliKy17IaBqcPK+KOGeMpcjupi8S49H9LuX/dFoLxPVhd2I8FnC6umDAVCwbvVlfwr40b92i+ZPE4wsdeCIBj4SvYl73dEzFFRERERERkAFNhKSJ7vbEZPu6ZNYnjivMBeL28hksWrGRZQ3OKk6XGlKwcfjhyLAD3LF/O/Lo92IQHiI8/gMiBpwPgfPuvWDcv2+OMIiIiIiIiMnCpsBQRAVxWKxeOHcYDh86gwOWgOhLl2mXreHD93rna8sTiocwtLCJhmty+ehnrW5r2aL7o/icTm3QIhpnE/dLdWGpKeyipiIiIiIiIDDQqLEVEPmNmfjb3z57MN4vaV1u+tpeutjQMg4vHTGROQQGRZJIbVy6mfE92DjcMwkdfQHzwBIxoCNezv8Vs2XuvFyoiIiIiIiJfToWliMjnuG3tqy1vnjaO/L14taXNYuHW/fdnlM9PUyzG9SsW0xSNdn9Cq43QSb8kmT0IS3Mt0b/dAJE9KEFFRERERERkQFJhKSLyJaZlZ3DfrMkcW5QHdKy2XLh3rbb02GzcMGUfCpwuKsJBfrNqCeHEHpS2bh/Bb19B0pOBuX0jrufvgvgelKAiIiIiIiIy4KiwFBH5Ch6blZ+MHd6+2tLpoDrcsdpy3RaC8Xiq4/WJHIeTG6fMxG+zs66liTvWLCdhJrs9n5ldSPjUq8DpxrZtFa5X7odk9+cTERERERGRgUWFpYhIJ0zLzuC+2Z+utny1vIbvvvEJH9U0YJpmitP1vsEeL9dNmoHDYmF+fQ0PbVi7R8edLBiB/fu/xrTasJfMx/nWI7AXfI8iIiIiIiLpqLm5iZtuuo5vfvNIjjnmcK666pfU1tamLI8KSxGRTtpltaXLQWUwwq0rN3DDihLKg+FUx+t1EzKzuGz8FAzg9Yoy/lW6eY/ms46aRvj4SzENA8eKd3B88GTPBBUREREREZEuueaaXxEKhXjqqed59tmXsVgs3HHHzSnLY0vZJ4uI9FPTsjN4cN/JvFxTxz/XbmNxfRMXLVjJKUMLOXXoIFxWa6oj9poDAgVcMGo8f9q4ln9s2UDA4eTIwuJuz5cYuy+Ruf+H680/4/zkBUx3BrFZx/VgYhERERERkdQyTZNoItSpsUnDQjyx55fMsltcGIbRqbFr165h1aqVvPTSG3i9PgCuuOLalK6wVGEpItINLquVH08exZyMDP60fiuLG5p5amsF/62s4//GDGW/3KxO/8+hvzmheCi1kTD/LtvCvSWryXY4mZkb6PZ8salHYIRacL7/BK53/4Hp9hOfdEgPJhYREREREUkN0zT50+KL2Na8sk8/d2jGFH449b5O/b10zZpVDB8+ghdffJ7nn3+GcDjEfvsdwMUX/6z3g34JFZYCQCwepLJuG01NbZim8Zlf0J/+wm5/rv010+qhsSXY8bqB8ZlxGDset98sFrC1+GgJBnfOZ2CAYdn5aMe8hgHBcIxwtA1MY5e5DMPSMb3lM+MNksk4ppn8XG6RvlHscXHD1LHMq23k4Q3bqI5EuWXlBmblZPJ/o4dS5HGlOmKv+P6IMdRGwrxXU8lta5bx22mzCQT83Z4vuu+JGMFmHItewfX6Hwm5vCRGzezBxCIiIiIiIqmR7l1Fc3MTGzeWMGHCRP72t8cIh8PcdNN13Hzz9dxxx90pyaTCUjBNk1c//gGtoYpUR+kB7cXmp4WopaPYbH/OYrFimmAY1vaa1bC0j9s59tOf7XYbiQQdz1mxGNb2MRbrZ8Z2PG9YcbucRGMmBlYsFutnXrPtHGMxrFgsNnxVHsKhZMdzdiwWW/vPFjsWw4bFYsNi2LBa7ESSflpa4h3j7FgtDiyGHavl08eGYU37PwAHMsMwmJOXzYycDP61tYLnSitZWN/E0gUrOWXoIE4bVpjqiD3OYhj8bNxkGqIRljc1cP3Kxfw1PwtHdyc0DCKHnYURasG++n+4X7qb0KlXkxg8oSdji4iIiIiI9CnDMLhgxgOEosFOjbfZLMTjfXtKuN3e/je5Sy/9JU6nE4/HywUX/IQLLjiXYDCIx+PZ4zxdpcJSMAyDQbmzqayfTyLZ8ZvCNPl0v17zM8+1/2yxGCSTyY5dgj870txlHJiYpolhtBejn47vGGEmPx278317slOwiWkmOubeg2n6HaOjwHRgtzkxsGO1OrFanNisTqwWB1aLc+dzVqsDm9WJf7ufaMSKzerCZnV/5ub69N7mwWZ1Ybe5U32Qac9ltXLOyMEcWRjgTyVbWdLQzFNby3m3qpZf7jOOCQ4nn1213N/ZLRaumTSdK5YtYEtbKz//8ENunTKTDFs3a0vDQvgbF2CEW7FtWoz7uTsJnnE9ZsGwng0uIiIiIiLShwzDwGHt3N+pbVYLFnPPC8uuGDFiBKZpEo/HcDqdACR2XkczNeWKCksBYN+JvyQQ8FNb24L5Nb8WDYNOj+3q+B1ja2qaOwrO5M4Sc0fZaXb8xjXNJBgmuTleaus+M940gfZ7k2RHEZoEkmRleahvaMFMJjF3jDETmOx4b2Ln+/0ZTpqaWjuK2Y7XSJDcOW7HffvN43XQ0trWcYp6gmQyQfIzryfN+M7HDodBKBQikYxjmnESyVjHe2Lt45LxjvFxDCNBLBYlkYySTMZIJGMkzfbxnzJJJNvHxOKtX/8fpZusFgc2qwe7zYvd5sXRcd9+82C3+TruvdS35REJ23HYMnDYM3DaM7FZ3XvFStBij4sbp47lo9oGHtlQSlU4yq8+WsGkTB9njxjMpKzunzqdbrw2OzdM3ofLln7ClpYWrl62kJunzCTL4ezehFYboRN+hvuZW7BtX4f737cR+u6NsAenm4uIiIiIiMiXmz17f4qKirnttt9w9dU3EIlEePjhBzn44MPweLwpyaTCUtLSp9fLtHzNOHA5/bgclk6VoTmZfpKxLpSy9t4rZfd0bHJHwZmM7Swzk2YUf4adurp64okI8USURDLSfkvsuI8ST0ZIJqPYHUla25qJxUPEE2HiiVDHLfzpfTxE0owB7CxFI7HGr/9SdsNi2HDYM3DY/TjtO4rMDLIyA5hxLy5HDi5nDi5HNi5HNk5HFhajf+64bRgGB+blMDMnk39treCFsipWNbVy5dK17JOTydkjihntT80f/D0t4HRx89RZXLtiEVvaWrl6+UJumTqL7O6WlnYHoZN/heepG7HWbMP9r1swL7kLsPdobhEREREREQGbzcb99/+Z++77Pd/5zslEIlEOOugQfvrTy1KXKWWfLCJ7xGJYsVitYP10UxfDgEC2HyPRs8VpMhknkQzhyzCoqq4iFg8SjbURS7QRi3/21kosHiQWb8M0QrQG64nGWojEmkkmoyTNOOFoPeFo/a4fUP4lGbHgdGR2FJg5uJzZuB05BGqLIZGF25mP15WPy5H9teX255mmSSQZJ5iI0hqPEExECSWi+BIumptDnfv+TD+JtgReqxOvzYnH6sDyuRWkLquV748azNlTRvDgkhLeqqhlcX0Ti+ubmBPI5nsjihnq7f+n2w/xeHnokEP48bvvsS3YxlXLFnLr1FnkOLtZWrq8hE65Cs8T12Npqib6l+vglKvBpZWWIiIiIiIiPS0QyOPGG29LdYydVFiKyNeyWGxYrX4yvH6ifl+Xy1DTNEkkI0RjzURizR33LURjTUTjLVisIeqbKglHGjoKzQbC0UZMkh0/NwCbPv2ArZ/LZ9hwufKwOIuIOfKIWrMJWny0GS7idhetUZNgMk4wHqU1ESEYj9KWiJLo4euCGNBRXjrw2Vzt91YnPruTAn8Gw/xOLszIZn5NjAV1LXxc28C82gYOK8jlu8OLKHT37x3Fh/r93D59NlctXUBZqI2rli/glqmzCDi7d1ymL5vgaVfjefx6LBWbcT91E6HTrsX0ZPRwchEREREREUknKixFpNcZhtGxkY8Ljyv/c6/tfqVnMhknEmsiHG0gFKmnLVJHRaiW7aEm6hNBqkJtNMYTtJhW2gw3Qdwko1aIAsSBxk5ls2DgsTrw2Bx4rA6cdlund2RLGEmaIiFa4xFiHZs9tSYitCYiVEVadh1c9bk329y4zGHEEpn8t6qOd6tqmZzt4PjiXMZlZJNr+jqVId0UuT3cPm02Vy1fyPZQkKuWLeS2aXtQWmYVEjrzOrxP39x+evhTvyF0+rWY3qyeDS4iIiIiIiJpQ4WliKSVtniEslAjZaEGykINlIYaKAs2Uh5uJL5zRaSn/fa5s8ANwGcxyDCSeAnjTrZgizdgTbRiN2M4zBh2dr23kcDrysdvL8bvGkxxwXjsRhGZ3pE4HZlfmvPzRWs0Gac1HqE1HqEtHqE1EaU1HqYtHqUtESFijbO1sY6KcDNV4WZCyRBhYy3gxZIcDGYWKxpiLG8ox7QsweesZ5Q3i9G+fEb78hjjzafQldEvNi0q7Cgtr162gIpwkCuXLeDWqbPId3Xv1HcztxjHj24n/McrsdaVdZSWv8b0ZfdwchEREREREUkHKixFJCVCiSib2+rYHKylsqyZkoZqSoMNNMSCX/oeh8VKsSuLYZm5+HEScPjJc/oIOHzkOX3kOLzYLZ9u0mMYkJvrY3tFGc1tZbQEy2gJbqe1474lVEYs3kYwXEUwXEVV/WI2lL248/1uRy5Z/lFk+UaS6RvRfu8dhtX6xesyOiw2chw2chxf3Ehnd6fIN8fDVIabqQw3URlpZk1TG2sarATjTozkINpCBSwL17K0cQ0YiwHwWZ2M8uUx2pfHaG8+Y3x5FLm/vFRNpQKXm9umzebq5QupDIe4atkCbp02m4JulpaWvMGEzrwe91M3Ya0vx/PUjQRP/zWmP7eHk4uIiIiIiEiqqbAUkV5lmiZ10TY2tdWyua2WjW21bGqroSLcxJddCjPH7mGwJ5sh7myK3dkMcWcx2J1NntOP1WJ0erMgaD8d3eXIwmnPIi9r8heyRWJNHUVmGS3BUkLRUqrq1tEWriQUrSNUV0dF3fzPzGfF7ykmyzeKwYWTcNtGkZMxDrut8zt+G4ZBpt1Npt3NOH9B+5OD2/Msrm/msS1llLQEMcx8iOfjtoUIUUprvIFlTWUsayrbOZfX6mBSThGTPIOYljmYUb48rF3cgKi35Lvc7aeHL1u4c6XlbVNnUej2dGs+M7uQ4BnX4/nXTVgaKvE82VFaZub1cHIRERERERFJJRWWItJjTNOkPNxESWsV5ZXNrKotZ2NrDc3x8G7H5zq8jPQGmJRXRB4+il3txaTH5uiTvDvKTJejvcz87ErIaKyNxtbNNLVuorF1E40t7ffReAvNbdtobtvGtqr/7piJTO8wcjMnkJs5kdyM8WT5RmKxdO2PWMMwmJmbyczcDCoMk/+3chMf1zQQiruBsQxy2ZmYZWCzNrA5WM3mtjraElHm12xhPlsA8NucTMsczPSsIczIHJLy08gDThe3TZvFNR3XtLyyY6VlsaebpWVWPsEzr8fz1G+wNFV3rLS8DjMr/+vfLCIiIiIiIv2CCksR6bbaSCvrW6tY31rN+pZqNrRW05qIfGGcBYMhnmxGegMdtzxGegNk2t1fuulOqtltXvKyJu+yKtM0TUKR2vYCs3UjbZFNlFevoC1cSVPbFpratrCp/DUArBYnORljyc2YQCBrAg73vkDnNtIxDIOpgQyumjyaymCEl7dX8WZFLdXhGNWV4LdlcEzRaK4am0tbspWtyXo+KNvAsqbttMQjfFC3kQ/qNgJQ4PQzPWsI0zOHMD1rMFmO7p2SvScCThe3TW0/Pbws1MZVyxa0b8SDv1vzmRmB9tJyx0rLHaeHZxf2cHIRERERERFJBRWWItIpzbEQJVU1LNy+paOgrKJ+N9ebtBtWRvoCTAkUU2zNZIQnwDBPLk5r///jxjAMPK48PK48ivP221m0BsP11DWvoa5pDXVNq6lrXkcs3kpN4wpqGlfANvhgOXhdhRTkzCA/ezoFOfvgdX39qsACt5Mfjh7Kd4YX83ZlDS+WVVMVjvD0tgqeLa3k4Pxszpk8lsP9Y0mYJiWt1SxpLGVpYylrWiqpirTwRtVq3qhaDcBIb4AjB49nhmcww9y5fbb6Msfp3LnScluwjSuXLeTBrEPw073PN/25BM+4Dve/bt7lmpbkFvVwchEREREREelr/b9BEJEeF0sm2NhWw7qWKta1VLG+tYrycNMXxlkwGObJYYyvgLH+fMb68hnmycVhtablqsne4nbmMDjvQAbnHQiAaSZpDpZ2FJhrqGteQ2PLRtrClWwqf23nKkyfu5iCnOnkZ8+gIHsGHlfgSz/DY7Ny4uBCjisuYH5tIy+UVbKqqZV3q+p5t6qe4V43RxYGOKwgl+8MKeQ7Q2YTTsRY2VzO0sZSljaVsamttv227gMAil1ZHBQYxYG5oxjlzev18jLb4eTWqbO4dsUitrS1csG773LlhGlMzcrp1nymL4fQjtKyrgzPU78hdMavITC+h5OLiIiIiIhIX1JhKbKXM02T8lBTeznZWsXalio2tdUQN5NfGDvUm81ITx5jffmM8RUwyhvAZbWnIHV6MwwLmd5hZHqHMbLoGAwDMjKtrN34MVX1S6iqX0J9y3paQ9tp3b6djdtfAcDvGUJB9gwKcqbj8R3K7v6IthoGc/KymZOXzYaWNl4sq+LDmga2tIX4y8ZSHt1UxqycTI4sDDA7N5NZ2cOYlT0MgMZokMVN21jQvJWPqjaxPdzIU2WLeKpsEYXODA7sKC/H+Qp6rbzMcji5Zeosblq5hLUtTfx6xSIuGj2BowcN7tZ8pjervbR8+masNdtwP/kbkj+6FWzaPVxERERERKS/UmEpspdpioVY37FqctP6OlbWt1/38PMybC7G+QsY5y9kvK+AsRn5jCjM22tWTfY0h91DUWBfBuXuC0As3kZ1wwqqG9oLzIaWElqCpbQES9mw/UU+XGEQyJxAUWAORYH9yfaP+UKJONrv5ZcTR3JVhovn12zj7Ypa1re08UldI5/UNZJpt3FoQS5HFQYY4fOQ5fBwZP54zpg4m22Vdcyv38IHdRtZ2LCVykgz/96+hH9vX0Kew8eBuaM4MDCKQ3LH9vh3kWl3cOu0WTy0ZR1vlZVxb8lqykJBvj9iDNZuFKWmJ4Pg6b/G8/QtWKu3EP3TVVhOuZJEwcgezy4iIiIiIjLQvPnma9x55627PBeLxTAMg//+9+OUZFJhKTKAhRMxNrTWfGZjnCoqI81fGGc3rIzyBRjnK+woKQsodO66u3QKN5oekOw2L8V5+1Octz8A0VgL1Y3LqapfQnXDEhpaNlDbtJraptUs3/gX3I5cigL7U5S3P4U5s7DbPt1lO8Nh55vF+RxblM/WthDvVNby36o6GqIxXiyr4sWyKkb5PO2njBfmEgA8NgeH5o3l0LyxhBMxFjZs5cO6jcyv30JNtJXnK5bxfMUy8jf4OSIwjqPyxlPkzuqx43dardy0777kW508tnUjz5ZtoTzUxmXjp+DqzvVO3X6Cp1+L55lbsVZuwv3kjYSOu4TE6Fk9lllERERERGQgOvroYzn66GN3Pq6pqeb888/hJz+5NGWZVFiKDBCxZILSUD3vt25kccVW1rVUsTVYT5IvLocc4s5mrC+ffQYNpdjIYoQngN1iTUFq2cFh9++8DqZhgMsTYuX6t9leM4/K+kWEonVsLH+FjeWvYDFs5GdPoygwh+K8/QkEJu6cZ5jXzXmjhnDOiMEsbmji7Ypa5tc1srE1yMYN2/jrxlL2K8xhv6wM9s3Nwmuz4bLaOSgwmoMCo4km4yxuKOWDug18Ur+Z6lALT5Yu5MnShUzKKGJu/ngODozGbXXs8TEbhsF3h4+iyO3h7nWrmFdXwxXLFvDrSTMIOF1dn9DlI3T6tWS8dj/JksW4n7+LyOFnE9vnWDXuIiIiIiKSMqZpEk588czG3bEaFhKJL16iraucFke3LvVlmiY33XQdBxxwEN/4xjf3OEd3qbAU6YeiyThb2urY2FbDhtYaNrTVsLmtdrfXncx1eBnnK2CMP7/93peP1+bEMNirNsbpb3yefEYPPoFRxSeQSEapblhGee08ttd8TGtoO5X1i6isX8Ti9feTtXwYxYGDGJJ/2M5Tx60Wg9m5WczOzaI5Fue9qjr+U1nLxtYgH1bU8WFFHTbDYJ+cTA7Ky2a/QDYemxWHxcb+uSPYP3cEMTPOqlglz25YzOKGUlY1l7OquZw/bnqfgwKjmJs/gckZRXt8vctD8weR73Rz8+olbGxt4RdLPuG6STMY7c/o+mROD/bzrqf1qfuwL3sb13//jqWhksgR3weV8iIiIiIi0sdM0+Rny3/PqpZNffq5E/0juWviz7v897U33niVzZs3cfvtd/VSss7Z48Kyurqa/Px8AEpLS3niiSew2+2ceuqpDBkyZI8DiuztwvEYa5or24vJ1mo2tNWwNVhPYjflpM/qZELOIEY6c9t37vblE3D6UpBaepLV4mBQ7mwG5c5m5rhLaG4rpbz2Y7bXzqOmYRmNLVtpbNnKqs2P4XMXMaTgUIbmH0pOxngMwyDDbuOEwQWcMLiAbcEQi1tbeXNLJaXBMPPrGplf14jd2NJeXubnsG9u1s7ycm7xBGY4B1MTbuU/NWt5q2oN5eEm3q5ey9vVaxnkyuSo/PHMLRhPAH+3j3FCZha/n7E/N65czLZgG1csm88vx0/hgEBBl+cyrDYic39IIqsQ53uP4Vj6JpamakIn/BQc7m5nFBERERER6ZZ+csZXMpnk0Uf/wjnn/ACPx5vSLN0uLJuamvjRj35ELBbj3//+N83NzZx55pnU1dUB8OSTT/LUU08xfPjwnsoqMqAlTZOqSDNb2urYHKxjS1sdW4N1bA817va07gybi9G+PEZ58xjjy2eUL49Brgzy8jK0anKAy/AOIcM7hPHDTieeaKMlsoxVG15ne808WkPlrNnyBGu2PIHHVcDQ/EMZUnAogcyJGIaFYV43M4flc3JBHltaQ3xQXc8HNfWUBcM7N+uxd6y8PLggh2Mz2wu+gNPHGYNncXrxTFa3VPJW1Rrery2hItzEP7Z9wj+3fcJ+W0dwVM549ssZgdWwdPm4Clxufjd9P367ZhmLGuq4dfUyzh0xhlOHDO/6l2QYxGYfj5mVj+uV+7FtXorniesJffsKTL92EBcRERERkb5hGAZ3T/kFbdFwp8ZbbRYS8dScEr548ULq6mo5/viT9vjz91S3C8v777+fZcuWce655wLw/PPPU1dXxy9+8QumTp3KZZddxoMPPsgdd9zRU1lFBoymaIjlTdvby8m2WrYE69jaVk8oGdvt+Gy7Z2c5OdqXz2hfHnkO3xf+8Okn/2gjPchu8zK28BvkeA8gFg9RXvsJ26reo7z2Y4LhKtZu+xdrt/0LtzOPIfmHMKzwUHJzDwDar3c5bEQx3x1exNa2EB/UNPBBdT3bQ5+Wl/es3cy0rAz2D2SzXyCLLIedSRmDmJQxiB+PPJgP6zbyVvUaljdtZ171ZuZVbybP4ePYwsl8o2Ai2Q7P1xzBrjw2G9dNnsHDG9fxcnkpj24uYXuojetz9+vW9xMfsy/BM67H/dwdWGu24fnntYS+/SuSBSO6NZ+IiIiIiEhXGYaBy+rs1Fib1bLby731hXfffYdDDjkMtzv1Z6Z1u7D873//y2mnncYVV1wBwHvvvUdmZibnn38+FouFM888k6eeeqrHgor0NwkzSU2kldJQA9tDDZQGG9geaqQ01EBDLLjb99gMC0M9OYzw5DLcm8sIby77DB4GrSagNlK+ms3qZmjBYQwtOIx4IkJF3XxKq96jrOZDQpEa1pf+m/Wl/+bjVYUMzT+C4YPmkuUbiWEYDPd5GO7zcNbwIra0ta+8/LCmge2hMAvrm1hY38QD62FCpo85gWz2D2RT6HZyZP54jswfT1WkmfeaS3hu81Jqoq38fds8Hi+dz4G5ozh+0FQm+gs7/a97VsPCj0dPoNjt5eGNa3mrspya99/n56Mnkuvo+mY8yUGjCJ51c3tpWVuK54kbCB2vHcRFREREREQ+a8WKpZx66pmpjgHsQWFZVVXF1KlTAYhGoyxatIgDDzwQi6X9NMDCwkKampp6JqVImjJNk+Z4mKpIM/NDW1lTXUFZsIHSUAPloSZiZuJL31vg9DPck8twb4DhnvZyssiVie0zG4MYBgTcfmrbdIq3dI3N6mRI/sEMyT+YRDJKZd1CtlW9R1nNB7QGK1m95XFWb3mcLN9ohg86iuGFR+Jx5WMYBiN8Hkb4PJw9spgWh4VXS7bzcU0DG1uDrG5qZXVTK3/ZWMoIr5s5ee3l5Qifn4smHsbJgem8X7OBVypXsKalkvdqS3ivtoSR3gDHFU7m8LxxuKz2Th3DCcVDKXJ7uH3NMpbW1nJx48dcOnYScwL5Xf4+zMw8gt+5EfdLd2PbsnznDuLxmcd2eS4REREREZGBqLx8O4FAXqpjAHtQWGZnZ9PS0gLAokWLCIfDHHDAATtfLy0tJTdX1wmT/i+ciFEVaaYy3ExVuP2+MvLpz192Gje0r5gsdmcx2J3dcctiiCebaYOHEG6KqoSUPmG1OCjOO4DivANIJiM0R5axfP0LlNfMo7F1A0tLNrC05E8U5MxgeOFchuQfgsPefsmBkZk+zhxexBnDiqgOR5hX28i82gZWNbawuS3E5rYQj28pp9Dl5PCh+Uz3ejk0byxH5I9jQ2s1L1es5L3a9Wxqq+W+je/y1y0fcVT+BI4fNLlTm/TMzAlwzz778/uSVaxtbOSW1Us5ZtBgzh85Dpe1i7t+Oz2ETv4VzncexdGxg3i0sRLztIu7+c2KiIiIiIgMHG+99X6qI+zU7cJy4sSJ/Pvf/2bWrFk89NBDWCwWDj/8cACWL1/OU089xZw5c3osqEhviCUT1EfbqI22URdtpTbSSm20lfpoGw2JIGWtjV96+vZn5Tq8DPPnUmD3M9jVXkwO9mST7/R/YfMRwwCf3UmYaG8dlsiXslqdjBl6JNmefQlHmymtepfNFW9R07icqvrFVNUvZsHaPzA47wBGDDqa7Owjd7433+XkxMEFnDi4gKZojAV1jXxc28iS+iYqwxGeWF/KE0CW3ca+gSzmBLK5aNRh/HDEAbxdtZZXKldQHm7ihYplvFCxjP3LRnBc3mRmZA7F8hWnixd7vDxy+OH8YeES/l26hdcryljZ2MCvJkxhpC+ji1+AjchRPyS5YwfxJW8Sa63BmPsjTE9W975UERERERER6VHdLiwvvfRSzj33XE4//XRM0+TUU0+lqKiIjz/+mPPOO4+MjAwuvPDCnswq0immaRJKxGiKhWiMBWnsuG+KhWjbHmV7UyO1kVbqom2dKiMBvFYHha5MClx+Cp0ZHT9nUOjMIN/px2WzEQj4tTu39CtOewajB5/I6MEn0hqqYGvl22yueIvmtq1sq3qXbVXvMn9NFkMLjmJk0TFk+8fsfG+mw85Rg/I4alAeoXiCJQ1NLG5p5YPyWhpjcd6sqOXNilrcVgszczKZExjCH6ZOYn1rOa9UrmR+/Zadm/QMcWdzUtE0jviK08XtFgs/GDmWGVm5/H7dSspCbfxiySecO2IsJxZ/deH5BZ/bQTxZsgT39isIf/MiEsOn7unXKiIiIiIiIntoj1dYvvXWWxQWFnLMMccAMGzYME4//XTOPfdcRo4c2WNBZe9kmiaRZJzWeISWeLjjFml/HAvTkggT2RansqWpo5gM0RQLEUnGO/0ZNsNCwOkj1+Ej4PAScPgIOH2MDATwxOzkOzPw27q+0YdIf+JzD2LSiLOZOPx7NLSUsKXiLbZW/YdQpI51255h3bZnyPaPZmTRsQwvnIvTkbnzvW6blQPzczhp4jAqqptY0dCy89Tx+misfffxmgZshsHU7Az2D8zmzMFzmN9WwnNbllIaauD+je/y/7Z+zDEFkzhh0FQCTt9uc07PzuW+mXO4d/0qPqmr4ZFN61jcUMvPx00m29G5Xfd2iI/Zl9DZt+B99X4sVVtxP3Mb0X1PJHrgaWDt9v8eRUREREREZA91+29kCxYsYNSoUfzgBz/Y5fmioiJ+85vfUF5eznPPPcfJJ5+8xyGlf4onE0SScSLJGK0tUba3NBCMRwkmorR13O+8dTwOJaJEjAQN4WB7IRkPEzeT3fp8l8VOlt1Npt1NtsNDlt1NUWYW3oSDXIePXIeXgNNHhs31hd2LDQOtmJS9kmEY5GSMJSdjLDPG/ohgfDVL1vybsuoPaWjZwKJ197Fk/UMU5x3IyKJjGZQ7G4vl0/+V2C0WZuRkMiMnkx+NGUpJSxvzahv4uKaR7aEwi+ubWFzfhAFMyc3mjEHHE6WGd2qXUxlu5unti3m2fCkH5Y7iW0XTGecv+ELGTLuDaydO57WKso7Cso6LF33Ez8ZNZt/crl0gOhkYguOSP9D69IPYl72Nc/4L2EpXEzr+EszMrm/uIyIiIiIiInuu24XlOeecw5133snxxx+/29c//PBDbr75ZhWW/URzLMTG6lpqGlqIJhNEk3GiyTiRZIJYMk4kGSfWUUDGzATGVoOmYJBwIk44GWu/T8QIJ2NEOp7rbtG4O1bDgt/mxG9z4bM58XX87Lc5KcrKwhGzkmnzkOVwk2VvLyc/f2qpSkiRrrFYbAwvOhCfYyrhSBNbKv/DpvJXaWgpobT6PUqr38PtyGV40dGMKj6WQGDyru83DMZl+BiX4eP7I4dQ2hZiXm0D82obWd/SxvK6JpbXNQEwyjeTSTlQGtnI+rZtO3cXn+Av5OTi6ZyQM22XuQ3D4JtFQ5icmc0da5ezpa2VG1cu4cTioVyWvU+XjtOwO4kcfT7xYVNwvfEnrBUleP9+JeGjLyA+bv89+xJFRERERESkyzpdWG7bto2HHnpo52PTNHnqqaf48MMPvzDWNE3mz5+P1+vtmZTSq0zT5OKlT1ETae2V+S0Y+OxOXBY7Hquj/WbruLd+8bnC7EwImXitTvz29lLSZbF/YRUkqIQU6StORybjhn6bcUO/TUPLBjaVv8aWircIRetYs+UJ1mx5gsK1UxhWcAxDC47AbvN8YY4hXjdDvG5OG1ZEXSTK8lCQN7dUsrqxhY2tQTa2AgxisHsoTlsLW0IbWNNcyZqW1/nr1o84rmAy3yiYhM/26anfQ70+fj9jPx7dXMKL27fx4vZtrG5p5JLRExnVxQ154mP3o61gBO5X7sNaXoL7pbuJbj2SyOHngL1rp5uLiIiIiIhI93W6sBw6dChbt25l8eLFQPvqlgULFrBgwYLdjrdarVx++eU9k1J6lWEYHJw7muWt27EmLdgtVpwW25feOyw2sv1eEuEELosdp8WGy/rpvctix2W1ddzbsVss5OVldKpUVAEpkv6y/aOZOe4Spo/5MeW1H7Np+2uU131CZd0KKutWsGjd/QwtOIxRxccRyJy8239sCLgcnD44lyOys6iPxPiktoGPahpY3thCRSgGuIDJZNpNImYNlW3V/GXzRzy+bQFzCyZw0qBpDHK3X0fTYbFywajx7JOdy93rVrGhuZmfLZ7HNwcN4ezho/HZd7+Rz+6YmfkEz7gex0fP4PjkBRzL/4N1+zrCx/8UM39ID32DIiIiIiIi8lW6dEr4n//8Z5qamjBNk6OOOoqrr76aI4888gvjrFYr2dnZOJ1akdJf/N/IgzpdFHa1VOzK5r0i0n9YLXaG5B/CkPxDCEfrqG56j+Xrn6M5uI1N5a+xqfw1MrxDGVV0HCOKvoHLkb3bebIddo4pyueYonxaYnHm1zXyUU0DS+qbaI0B5GMlH6sRI5ys4cXyDbxUvpz9c0fyraJpTM4owjAMZuXkcf+sOfyjbBNvlJbySkUpH9RW8oMRYzmioGi3xenuD8xG9OAzSQydhOvVB7DWleF57Goih38f88hv9dTXJyIiIiIiIl+iS4Wlz+fD52vfufW2225j9uzZFBcX90owERHpP9zOXGZO/D5D806mumEFm8pfYWvluzS3bWNJyUMs3fBnivMOYFTRcQzKnY31S3bh9tttHFkY4MjCAMF4goV1jXxU28DC+iYiCTsWswiSRZiEmVdTz8d1bzDK6+Xk4ukcHBhNtsPJb/bdl0OzC3howxpKg238Yf0q3qjczoWjJzDC5+/0MSWGTSF4zm9xvfYgti3LcL31CLGKNRiHnIPpyfz6CURERERERKRbur3pjjbTERGRzzMMg/zsqeRnT2XmuEvZWvkOG8tfpa5pNWXV71NW/T5uZ4CRRccwy3kakPWlc3lsVg4pyOXQwly8WR7eWF/G+9X1LKhrIpp0YSTby8vNTWHualnDw47FfKt4NGdnzGFadg737jOHF7dv5Ymtm1jd3MhPF8/jhOIhnDVsNB5b5/73Z3ozCZ1yBfaFr+B8/0mSKz/EU7KUyCHfITb1CDAsPfPFiYiIiIiIyE7dLiwBnnvuOZ5++mlqa2tJJBJfeN0wDN5+++09+QgREemn7DYvowefwOjBJ9DYuomN219lS8WbhCK1rNr8T1Zt/icF2dMZUXQsQwsOxWZ1f+lcbpuVg/JzODAvh3AiwYK6Jj6ormdhfWNHeVlMaxj+sTHMY1ueZf9AJt8dOolThozgkLxBPLJpHR/WVvHC9m38r6aSH44cx6F5hZ07TdywEJt9Asmhk/D+5y9QvhHXW49gX/ku4bnnk8wf3nNfmoiIiIiISAqsW7eWe++9i40bS3A6nRx++Fx+8pNLcTgcKcnT7cLy73//O7feeiuGYZCbm7vbAzC1a0q/8fdlBqtrgySTnbvGm8XS+bFdHZ8uc3c5hxEkaXZy7l4am85zf9W7rNZPv+vdjft8p2TrGG+a7a8Znxtn7JjHAJstSDLx6dyfH2987t5hDxGPG1h2PP+Z1z7/nNsVJhY12l/reM5ifHozPvOzzxMlEjY6HptYDbBYaL/vuFn/P3tvHibJcdd5fyIys7Luqu6unpme+5ZmdFmSZUk+8LVYtsHIGJtjbWDNLrvgBRbYZf28PObhXTA2rGEXm13AzyIvr8E2BmNOG3xfsmXLlq3DGh1zH9093V3ddR95Rbx/ZHb1PdM9mpFGIj7zxERk5C8jIzOjuqq+9Yv4JcdYEsqNkG4nrrOSOkuALRfKUoIjQbmKZn9hvy0X2rpaKOf3cus1P8fzDvx7xme+yvHxTzI5+02mag8yVXuQbz3+XnZveSV7t72WkeKhCwqJacviJZuGecmmYXphxLfm6nx5eo5vztaJdBodjXHfFHxt6gibMgGv376D/3roBh6qbeOPjz/ORK/L7z7+CJ+aPMfbDhyiUlnfNHG1ZS+pn/ufND/716Tu/UusyWNk/+z/Ibjl1Xgv+mFIrS24GgwGg8FgMBgMhn+5aK3pR8G6bC0hiSL1lM/pSnvd6/grpfiv//UXectb/g1/8Afvp1qd4Rd/8W2Uy2X+zb/5d0+5L5fCJQuWH/nIR9i7dy8f+MAH2LJly+Xsk+FpRms4Ngf1nubCstKSozZgu1H7q6Vt04+nr+2rpR8AagP20QZsg0W26znGI5ZI10NvVVtb6oF4OS9k2hIyqR5CC2wJjhWLnvP54rqUpSmf9wn68bZrQcqK96cWJdcC14ZQXfxHKkum2Ln55eza8nLcTIdvPfrXnBj/J9q9CY6N/wPHxv+BUm43e7e+lt1j30vGHb5gexnb4iWbRnjJphF6YcQ3Z+t8emaKh6ttIMNML8P/OdrgA8e/yk1DWX7p4HU80qjx0TMneKRR4+ceuI831ffx/aPbKDsXDxQnLIvg1tcQHLwd9wsfxHni66Qe+CfsJ76B9/KfIDx4+9WlFBsMBoPBYDAYDIZnFK01v/TgX3OkOfm0nvdwYYz33PCGdYmWrVaT2dkqWquB86GUEtdNX+lursklC5bnzp3j7W9/uxErnwMIAe/4Ho3vZKnXO+uKEl4u59Zlu1H7q6Xtq60fjY30YyhHvbbOttdpeyltly5wjYvrYtss9Xp3UD/YvXw7oVTKUm/E9ouP0UlhsX2xlKXR6KL0wj69yGa+PL9dKGSoN3poHUuX8+dYfJxK8mwuTavVJ0ps1LKkEXGuwU2n6HR9Ig1KxfsjvSxfVG/ZFn0vIlLxdpjk0aq5IFR6hfdrqAThaj/MddYrygpioXW9wmkXWwpcC9J2LGKmEzHTtZO6wT7NaHmEKPUT7Nr/4/T7x6jOfZHq3JeYbc9Qf/KPePDY+9lWuZN9217L8PC/uujZM7bFS7eM8EPX7+bM+TqfmpjgExMTnO9BpNJ8e1bx7dljFJyIl47uYMbr8J16lb84doy/OXGC79u6gzds3005dXHhUueH6b/uFwmuf4j05z6ArE+R+YffJ9zzPPqvfCu6vHmd98xgMBgMBoPBYDA817naXRpKpTI/8iP/mv/1v36f//2/30sURbzkJS/lR37kXz9jfbpkwXJkZIQwDC9nXwzPICkLtg5Lqop1iVGVddpu1P5qaftZ3Y8hSS5aZ9vrtL2ktjdyjSMWVb2BflQsqnKdbVcsqs5G2rapVtfbtkO12r+ArV5km6Ja9TbQjwzVamud/chTrbaIlCZULEnRsu1QQzafYbbWI4ggUCzkSTlUYlCWjkOrG+CH4EXgL0qBAi+Myzp5C54XSTsXne0gAJ8FMfSaJP2HZFth0cEe72BNdHAfeIBSOk2lUGE4WySfglyS8k5SdiCfaI1Z2+L1O3bw+h07qPZ7fPDUk9xXrdMLM7QCi89MNgGouMPYluZ8r87Hz53mExNned22nfzg9t2UnIuv1RLtuYnOT76H1Df+ltQ3/x775IPk/vS/4N/xBoLbvv+ixxsMBoPBYDAYDIbnNkII/sfz3kjH99dlb9mSaFXPk42x0SnhqZTLL/3Sf+X7vu8HOHfuLL/6q7/CPfe8n5/+6Z99yn25FC5ZsHzNa17Dxz/+cX78x38cy7IuZ58MBoPBcAlIsTBNey0Goqx7IVF2sdDqUq36FxROtYZIa/LlPJPTbXpBLGT2ozhfWhaDsrJs6u2QXgjdAHoB9MJY9ARJRIGIAmjoRlDrwKnOxe9DPtUhnxIUUlB0oehmOezexO07YcKf5kuzpznb9YhwmfWSa6WMKxX9qM/HzsTC5fdv3ckPbt9F8WLCpZPCf/EPExx+MenP3oN95lHcez+Kc+QrRD/w01C5lqv/N1WDwWAwGAwGg8FwpRBCkLacddnaliTUT12w3Ahf/vIX+NKXPs+HP/zXAOzdu4+f+qmf5vd//3effYLlK17xCj7zmc/wpje9iVe/+tVUKhWkXDlt8PWvf/1T6Z/BYDAYrnKEiNe4LLgCLwP6gsucLBZD06t6kQaRHoiY/RA6fsRc/yxPnn2UmVaVQOcJKBHqEthjaFHBUxl6YSwKtn1o+4LzS3uZ5FuALWxNtpTwCIVPJEIi4ZMSPpHwCMMef3Oqxj+cG+fuHdv5wW27KKQu/AFDD2+l96Z3YD/+Vdwv/BlyboLgT/8bmbEDeC96E9GuG8z6lgaDwWAwGAwGg+GqY2rqPEGwdJqcZdk4ziXLhk+ZSz7zj//4jw/KR44cAVjiaqq1RghhBEuDwWAwbAgnCexTTKZ3C2FRqdxAddduPL/FmakvcHLyb5mpPzI4xnYybNv6crZseg1bt97O2ekejT40fWh6gpYHDQ9aHjQ9aPmgtEBql5S+8JqVX3k04EuP9RnN+twyZjPswEgGRrNQycbrcw4QgvDQiwn33Iz7jb8l9eCnsSaPkv3Yuwi3H8J/0ZuIdhy+AnfNYDAYDAaDwWAwGC6NF7zgTt7//v/NBz/4Ad785p/k/PlJPvjBe3jVq177jPXpkgXLd7/73ZezHwaDwWAwXJSUU2D/9h9g//YfoNUd59TkZzg5+WnavXFOn/8kp89/kvyxzewYfRl7N7+c4eK1qzo1Kg0dX8fCZSrDqakeJ5s9Hqs3meoohCpgqSwSGwsHSznU2/D5o4rlQYiKrqaSXRAwR7OaSjbH6B1vZs+rfpj2P38Y58HPYp97DPujv0G483q8F/8wauvBp+emGQwGg8FgMBgMBsMF2LNnL7/zO/+T//N//ogPf/iD5HJ5XvWq1/BTP/Xvn7E+XbJg+YM/+IOXsx8Gg8FgMGyIQnYbN+z7N1y/9yepNh7l5MSnODP1edrdKR47/VEeO/1Rcpkxdm1+OTs3v5yhwoHBTAApoOBCMR2v6bkjDS/WGSBDPwr4SvUY/3T+UZ5sdrDDrdjRGLYuYOkUtkpjaxdbp5HapukJmh6cqM33bEEhzToum3P/hrGX/Cjbqw+x4/SX2TZ5hsqHfx215ya8F70JtWXf037vDAaDwWAwGAwGg2Ext912O7fddvsz3Y0BT3ky+tTUFF/84hcZHx/nh37oh8hms0xNTXH99ddfjv4ZDAaDwXBBhBCMlq9ntHw9z7/252j5D/Pdo5/k3PTX6PQmOXLqwxw59WEK2e3s3Pxydm1+OaX83jUj5qUth+/dfIjv3XyIk50q/3T+UT4/8zWagUDozQg1jCCeRi60ha1chq0cB3LDDFt52r5FtQu1HnQDwcm64CQZ4A7YfgcAKeUx5k+w9fPjbMmfZ9M1Bzl8Qw5Hm/A8BoPBYDAYDAaDwfCUBMsPfvCD/O7v/i6+7yOE4M4778TzPH72Z3+WN7/5zbzjHe+4XP00GAwGg+GiWJbL/h2voJy5jSDsM1G9j9Pnv8BE9T5a3XM8evLPePTkn1HM7WLn5peze8vLqVRuWLO9PbkKb9v3Un5q9wv5cvUoX6od5TuzD4LKItQYghK+FTJFl6nODADbsy4v31fhjpFhKuURHjvbYbIF59uC822Y6oCPy+n0Hk6n98QnOgYc65GSsK0o2FGE7SXNjiJsLVw48rvBYDAYDAaDwWAwPNe4ZMHyC1/4Au9617u4/fbbee1rX8uv//qvA7B3715uvvlmPvShD3H48GHe8IY3XLbOGgwGg8GwXmwrzc5kOngQdhmf+Rpnpr7ARPUbNDun+e6JP+W7J/6U4Uf3smX4TrZVXshI6RBSrFQH05bDXVsO8+brb+e758b5/PQTfH76CcZ7J0ANIfQmhM4CNue6Hn92cpw/OznOrkKW24aKvGBzmdccyGMJQaRgtqeZbMPUVJPpM+NM9l3G3e14pDlZh5N1mPe1FGi25GF7EbYXNTtLcFNer+ijwWAwGAwGg8FgMDxXuGTB8p577uHw4cN84AMfoNlsDgTLnTt38sEPfpAf+7Ef4yMf+YgRLA0Gg+EZJNKKThjQDn1aoU83CsiGNeqNLqHSKK2JtCJKcoVOyhqFolBP4/UCHGHhCIkjLRyZ5ELGZWGRsiRBF7zQJy1tLCEv3rmnEcfOsnvsX7F77F/hB23GZ77K6akvcH72m8w1TjDXOMGRkx8inRpia+VOto++iC0jt2JbmRVtbUkX+bEdt/Gj25/PE+0pPj/9BF+aOUorBKGG0XoYoVOAzelWl9OtLh87c56SY3PbSJkXjJS5ebjIppwFm4twYxFr9iy5R/+Cc989whl7K6fdPZzO7uNUdh8tMky2YbIN35xIJox/o0slK9hdhj1lze4y7CjG0dUNBoPBYDAYDAaD4dnOJQuWjz76KD/3cz+HZa38dmTbNnfffTfvfe97n1LnDAaDwbCSfhQy1e8w5XU432/TO6+YabVpBj7tcD7FImUnCp6RPmYsm7ydIms55GxnkOeSPG87bG2XsDxNyU5TSrmUHZe0tNdcW/JykXLy7Nl6F3u23oUftmh7D/PYic8yUb2fvl/jxMQnOTHxSSyZYvPwrWwffRHbRl9INj2ypB0hBNcWtnBtYQs/vefFPFA7w+dnHufrs08SqjxClYEMaAdwaAQhnz1f5bPnqzhCcNNQkdsrZW4bKVOp7MD9of9I6fbz3PzIF7n9wU8jT38IDdTsYU7ufgUnt76IM9YYZ5uCma6g2hVUu/CtRMS0hGZ7EfYMLYiYo1lWjZJuMBgMBoPBYDAYDFczT2kNS9d119zn+z5hGD6V5g0Gg+FfJJFWTHZbPFafZrLXToTJhVQP+htuc15AzFkOmZSDjjSWEEgEUggsIeNtIbCSbSkEqZRFu+8TqIhAKQKtLlj2VARALwrpRRd5Dzi1ssqVFiXHTVKasuNSTrlsq5VIh5IRJ8OIm2EklcGRT92d0HUKbBt7NSP5FxFGATO1hzk381XGZ75Kp3+eiep9TFTvg8dgpHgtB3a9nFLmRoYKB5Fy4S3UkRZ3jOzhjpE9tEOPe6vH+MLMkzzamETrAqg8kB6Il4G2+NZcg2/NNYDTHCjkeNnOTVzrptl362sJbn0N1smHSH3nUwyffJDhYx/j1mMfQw1tIbj5VVh3fT/fnlCcrMGpuuBkHdq+4HQDTjfgi8l08pwTC5c3bvfZmoZdJeOFaTAYDAaDwWAwGK5+LlmwPHjwIF/4whd4y1vesmKfUopPfvKTHDhw4Cl1zmAwGJ7rBCriTLfJsXZtkE526gRaXfC4nOWwJZ1jSzrPjnIJO4C8nRqkgp0ibzsUnBQ5K4Ut4ynaQkClUqBabaEvsgzipdhOTteTKegB3TCgEwV0woBO4u3ZCQO6Sd4TETOdWIBtBB5eInhOe12mve7SE5xbec6S4zKSisXLeRGz4mbYG42Q9gSjqSyutf63OUs6bBm5lS0jt3LrNT9Po32CczNfY7z6NWYbR5htPs7sI48D4Nh5Ng/fzJbhW9ky/HwK2e0Dz9C87fLqLdfxmrHrsAoWnzz6CPdWj/Od2hmUziXipQOkQDsILI62Ohx99CQABdvieUMlbhnezs3f/8tUurOkHvw0ziNfRNbO437+g/CVj3Lz/tu4/tCLiG65AS1tqj3NqRqcrAtO1eFsEzqB4NEZeHQmACS21OwqwYFh2D+s2TsEGWfdt8hgMBgMBoPBYDAYnhYuWbB8y1vewq/8yq/wzne+k1e/+tUAeJ7Ho48+yh/8wR/w6KOP8pu/+ZuXraOGK8uJJ+HbX+/j+SvnDopVNly3j+8J9OL9YpVjkkI63cfrr2YQl+c3hQDX9fC8lbbL2xTJf5mMR78vQC+a+igW9s9nQkAm69PricG54lwv247TuZxPt8uKfYiVtkLA7HRAp7PSTiyyQ4CU0O9GtFqL2pALNnJRm9ICN6Xo9ZJ2ZLJfLj234dlBoCJOthuxMNmJxclTnQbhKuKkIyWjbpYtbj4RJnNsTgTKzekcBTsFbExUfDpwpEXJsSg56QvardbvfhRSD/rUA49m4FH343Ij9OiKkPFmk1m/x6zXI9CKRuDRCDxOdOpLGz+6UCw7aTa5WTanc4N8s5tjU7KdsVd/GxRCUC7so1zYx/V7f5yeN8tE9WtUW9/m7OT9+GGbc9Nf4dz0VwDIpjcn4mUseKZTQwAMuVnu2nKYV20+TCf0+GbtNF+pHudbc5OEURZEDrQFOIl46dAKI74yM8dXZuYA2J3LcPPOV3Drdd/HjePfIffQp7Cq53AeuxfnsXtRmQLhwTvYcuhFjG47yG3bYnE6VHCuqTlZh7Mdh0cnQ5qe4HgNjteA4wJBPI18/zAcGNbsH4HKRh64wWAwGAwGg8FgMFwBLlmwfN3rXsfjjz/OPffcw4c+9CEAfvZnfxYArTVvfOMbeeMb33h5emm4omgNRx4WeF7EMnnyAmzEdqP24Qbb3oh9sIrtWsfGHknrx9+AfX8Dtr0L2gqhlwiZttUBRFxnxfVSJmLn4mRBJt0nCMVg25o/RoJl6YX6JDVrIZ3uwvbifZYFlh3nRkSNaYc+jzRmeLgxxeMPz3G8WSNaRVXM2yn25crszw+xPz/EgcIQ123fQm22c1WIkE8Xactmi5VnSzq/pH65uKm1phX6A/Gy6neZ9Xrxtt+jFnlMdFr0BgJonyfbc6ues+S47MgXGbUzjKXzjGUKbE3nGcvkKdqpgddkxh1h//bXcUflXzM9U2e28SRTc99icvZbVOuP0u1PDda+BCjn9zM2civ7+3fgyn2knBI52+Vlowd52ehBvCjk2/UzfHnmGN+YncGLMiAyoCVgDcRLsDjV6XGq0+NvAFcOc8Mt/54X5zWHTn2bfY9/EbvbIPXQZ0g99BlUoUJw6IWE174Ie3Qnu8uCPUNQqaSZmWkx3dEcm4Njc4Kjc1DtCs42Y2/ML5yKr3Vbqcv+suCaEc3BEcilrtQTNxgMBoPBYDAYDIbVeUprWP7Kr/wKd911F5/4xCc4deoUURSxfft27rrrLu68887L1UfDFUYIePErNF7fpd3ury2QzNcLyOfStDuLbFc5ZnHVvD06qder2CUektmsS6fjLenH8j5pvaCIZbMpuh0fvdhOL213vpxOO/R6QVynl7atF9tqcF2Hfj+I9yf1S45ZXKfBcWw8P2TeWU7r1ROAlJIwUIM6pdawV6ARaKWXXPPye6EjmPfRi0OsXA7hebV6j/UIrVJqHKeDtAS2HQuZdiJm2k6ynZRtW1MqBXh+vO3YSe4k+5O6qyzo9Kp4UchjrVkeqk/xYH2aY+0aatmLo2in2J8fYl8iTu7PD7HZzS0JNCMEV12U7asJIQRFx6XouOzJlZfti8XNmZkmrcBn2usOAhRN97tJ3mHK69KNgthLszaz6nlylsNYJj8QMLdm8hxiE4XAYqR4LZXSIa7b8+OEUY/p2iOcn/sW52cfoN4+NkiPnf4oAMXcTkZLN1ApX89o+QYK2e3cObKXO0f2EqqIR5rjfKc7wRfPnWXW06BzxF6XArBBO0gcPEWy9iUg91O48RpuSGlurp/illNfY09zHPf+v8e9/++JRrYTHnoR4aEXQqWAELApF6cX7ojHZb2vOZoImMfnYLwlGG9oxhuCL51e8MC8pgLXjmj2DUP6KX1yMBgMBoPBYDAYDFcjp0+f4r3v/V2OHPku2WyOu+9+Az/+429Fymfmu+lT/tpx4403cuONN16OvhieQcrDUKk4VKsXECwTYkFgfbYbtY9tU1Sr3kVs9Qbt521dqlX/stou2Kc3sNZfdgO2earVFkrpgYipluWLRc9yKcfsXAcVxXWLk15c1pDJpGk2+6gIokjE+yKIklwpiKKFspQW/X5EFEEUxvvmk1YLgptSAs+D9QmngvV4p1q2HoiY2WwPIQROijg5kEppnBSk5uuScqGgr5iXYqgUjzdnebA+xUP1aY40qyvWntyWKfC88iZetGMXYzrDaCp7xaNgG2JRs+C4FByXffmhVW3aoc+016HjRDwxPcNEr81Ev81kr03V79GJgsG6ogOejLO8nWJ7psC2TIHtmQLbM1vZvv3N3LD/PxCFTc7PPsBU7dvMtR6l1jxFs3OGZucMxyc+AYDrlBktXz8QMG8qHeRVB67j325rMd1v8a2509w7e5YjjSZ+5ILIoLQALMBGaAeBTSuM+FoIX0vthIM7KUrNjd4sz5v6Ljc3TrPn3o/i3vtRvG37SO28iWDvzagt+wa/AJTTcNtWuG1r/CLpBpqpIMM3T/Z5vAqT7QUPzM+eEEih2VOOBcxrRuI1MA0Gg8FgMBgMBsOzm263yy//8s/xghfcwW/91ntoNOq8/e2/RBRF/NRP/ftnpE/rFiwnJiYYHh4mnU4PttfD1q1bL61nBoNhCYP1KmUsWaxlM1yRKFZ6pa5mu1RIvvABsX1mTaFVKz0QL5WCQiFHdaZDEEAYxgLn4jwMRVyOwJY27U5IGBDbL8pVIoRGiT09aDcV65/a30UIQcolTilw58suuK5eKKch5SjCMPYGXY1m4PHNuUm+PjfOQ41pOmGwZP9IKsNNpU3cVN7M88qbqLjZq26dSUNM3k5RcFJUKgVudCtLno0XhZzvdwYC5mQ/FjPPe23O9zq0Q5/HW7M83ppd0qZEsDmdY1umwI7sqzi0882M+T5p7yy99hGq9e8y23wCL6hzbuZezs3cGx8nU2wevpZCZh/DxWu4s3CQuzZ/L1pInmxNcd/sae6bnWSyF6J1DoSd9Dde/1JoG4FNUwnudSrcu/1lsB0Kyud5jdPc0B7n8MNf58DX/w4nnSPa8zzCfbcQ7r4R3Oyg/7kUvGCrzd5cLPQ3+ponZuGJWcETVZjtLayB+cmjAkdqrtvSZ38Zrh2BrQWzJITBYDAYDAaDwbAcrTX9KFyXrS0kYXThIKzrwZXWup1lHn74QWq1Gr/8y2/HcRwymQw/8RM/xXvf+3u89a0//Yw43axbsHzlK1/Jf//v/53Xve51ALziFa+4aIeFEBw5cuSp9dBgMDwrEBJsGXtACgFDw5JIXUg4Xewlu7Z3ahRpwkT0nBcxM5kMs9Uevge+Lwh8CHzw/dhmUPZjz1GtBV4fvP6qPV+2Ha8ZatmadDoWMXFCanQ4F9U4E8zRtXr0pI+0XEquzXVDw7FAWdrEtkzBeFA+B3Atm125ErtypUHdvPB8bqrGeLfNeK/FuV6Tc71WUo7XzZzsxwLnt2qT/M34k4PjS84YOzLXsH1njmHRIxtO4HSfoN/8Nn7QYLL6MJM8PLC3pEu5EAuYLy0c5AevuRHL3cJDzQnub57nOzMz1HwFOrtMwLRB23EAH5niK0MH+MrQAQBsFXGgO83h1iSH7/0nDn/m/6MyOka092aivbegR5b+yFhKwwu2wQu2xS/OalfzRDURMGeh6QkenIh4cCL22Cy5mmsrcKgS56ULx14yGAwGg8FgMBie82it+eXvfJ4jzerTet7DxQr//YaXr+v7qVIKx7GxFwUlFUIyNzdLq9WiWCxeya6uyroFy9e//vXs3Llzybb5Um4wGK408wF93GQ7Fo1s8kUu6hkqBJTLOSYn2rFg6ZGInLF46XsC3wPPT+r74HkimfIu6LSh0wZwgDLbKLONPSvO44xr3AyczMD5DKSzkMlo0hlIZyCThaEh41r5XCFt2ezNl9mbLy+p11pTC/qc7bYY7zUZ77U4H3Y53qgx43WTqOYzfLc5v2amBRwmk7qRsaLLmBuR8atkvDNY3SNkomlmG0eYbSz88GfJFOX8Pl61+Xp+YNt2LHcX48rl280aD9dnmeqHaJ1GiNQqAqZNKC0ey4/xWH6Mv958CwDDfpvD5yY5/MRfcIgeqd17sDbtI9p+GJ1d+sGkkoXKTnjRztgDc7KtOdNL861THk/OQsMTfGMcvjGeBPApaA5V4NCo5sDIFXkcBoPBYDAYDAbDVc/Vrp7dcMNNuG6aP/7j/8Vb3/rT1Os1PvKRDwLg+94z0qd1C5aFwlKvod/+7d++Ih0yGAyGy4ltCzLZWDhcyYKIqLTm8dYsD3Vm+NKZs9S7IRmVJhOlyUZpdtrDbLeHGBZ5RGDT74PXE4nnpyAIoNVc3Pbyt6Qu6XTcl0wOslnIZDXZHIO6dNpMp302I4RgOJVhOJXhpvKmJUsBdMOAc70WZ7stznabnO01OdttMtlv01MhJ7ohJ7oAeeAwpA5jCcGoLRgWPQrRNCnvBPnwPF7zKLPNx5ace6c7yo35PWQ37aFmb+FYYHGkE3Cu5xMpF0QqCeAzH4XcBmwEFnOpPPemDnBv4oUpA8WO4zUOPPI37MNnX7nInq27yOw8hF7mcbqtCDftdbhzcx8/hBM1zWNVwZEZONsUcRCfFnz2pMCWmsObe+wvw6EKbDPTxw0Gg8FgMBgM/wIQQvB7N7+Sjh9c3BiwbUkYPr1TwguFAr/7u+/lD/7gf/KGN3wf27Zt59Wv/j4ee+wI+XzhKfflUli3YPnRj36Ua665ZhBg59ChQ7znPe/h+7//+69Y5wwGg+FKorXmRKfOl2bO8OXqWWa87mCfk5IcGipy5/AWXjC8lXJq8dxWjRAwMpLj/Pk2vQ70etBPUq8r4nxRnVKCfl/Q70Ntbr6dpW8eQmoyGcjmYHjEw7IF2VwsaubykMk8O6KlG1aSsRwO5Ic5kB9eUh8qxWS/zblek1nh8Xi1Ggua3SaeijgfaM7jAjvA3gF2PGqGbEVJt8iFU2SDCYpBjeLsd0nP3o8AhoGXIMlmt+LlD9LIbOdYL8s536YRCLR2gXkRc8ELE2yUkJzOjHA6M8Jn5zs6BWNn7md/0GJfxmFvpcKeXQcYGqoMrsWxkmA8Fc3rr4WWp3l8Fh6fETxWhVpf8PCk4uHJeBAX3dj78vBoPH286GIwGAwGg8FgMDwnEUKQttYnwdmWJNRPXbDcCEEQEEUR73vfHw9Ezr/5m4+xe/feQSybp5t1C5bpdJpPfvKT3HDDDeRyObTWzM3NXTT4jgm6YzAYrjbGey2+NHOGL82c4VyvNajPWg7fs3Unt+Q3c0t5MxnLuWA7Qog4cnkJCqXFe1ZO/87ncpw716bbgV4Hul2R5NDrxuKmVoJuB7odqE6HxNLUgqgpRCxezguY2bwmn4cojAijOFq68Vh7dmFLyY5skZ25YuyNORKv5aq0pup1OZN4Yp7txutlnu02aYY+c6FkjhKIEqQODtpLC0WZDvlomlw0Talfo9j7Dnn9RW5CcRMQIumldlFz9zDJGFOqQCtyQKcRpNBLREwLsBBYTLplJt0yXwGoAbWTDIdH2GeH7HAddg5X2LFlGzvzOdKWRcFdiECuNUx14unj95+Mp483l00f31HUHB6NBcx9wytulcFgMBgMBoPBYLhCaK35pV/6OX7u5/4T3/d9d/PEE4/zwQ9+gLe+9aefsT6tW7B89atfzUc/+lHuvvtuIP6i/u53v5t3v/vdax5jgu48e1Bei9654wTNfqy1CJkoH2JJWQiBkBJf5IkaPfT8fgRCWkvthQQhEUIQeQId9NDIQf38PoPh6aDqdflK9SxfmjnD0XZtUJ+SFrcNjfGy0Z3cNjLG1k3lyx7NWwjIZAVDw1Aemq9degKloN/TsaDZBbTLzLRPtxOvo9ntgNYL62rOTMGCmNkHJI6jyRUgX4gFzXxBJ3kcPMi83J49SCHYlM6xKZ3j+UNjS/Y1gz6NVMiRyal4inkS9Geq36GvJecpgCyA3Dc4RqApCI+8qpGPZihEDQqd4xzSD3CbbqEQ1GSZGWuMWWcfNT1MR2dROgW4ifdlLF7GIqYNSObsDHPANz1gsg2TTwCwGZ/drsPOcpmdQ8PsymfZkUtzwx6H2zfF08eP1zSPzQiOVOFcU3C2KTjbhE8dF7iW5vqxPvtK8fTxzTkzfg0Gg8FgMBgMhitFKpXit3/793jf+/4H733v/2BoaIg3v/kn+IEf+MFnrE/rFizf8Y53cPDgQZ544gl83+dv//Zvef7zn8+OHTuuZP8MTwNaa5p//++ot6fWfUxjg+eor7VDyBWpLq1Y2JQWCAshFspIC5HkseBp0XNdgojYVtqQJDE4xh7U63yWnqcTGwdhOcn+JLdSSe7QaRUJOiHIFFipeF+Sz5eRjhFdr2JagcdXTo7ziZNP8khjZiARSgQ3D23mZaM7uWN4G1k79qR8Jh+llAy8J+O1D1NUq95AONUKej09EC877cQbsw29nqTT1gSBoD4H9VWmnFt27I2ZL8DoZh/bicv5IqRST/vlGp4CpVSafZUCO8kvEdZ9FTHRi6OXn+01OddtDcq9KKSp0zTFGNhLBVCBpkiXXDRLQdfY5J1gr36QnG6gUTRlgarYzKy1lZYYpU8OtAs6RRyQyhp4YsbemJIpUkx58I2pBkzF7xgSzZaUZEc2y/ZCge3ZNIe2ZvhX+9OoyObxKhypCh6bgZYveOBcxAPn4unjQ+kk+vio5toRKJjp4waDwWAwGAwGw2Xlec+7hQ984M+f6W4MWLdgOb9e5Zvf/GYA/vZv/5Yf+ZEf4XWve90V65zh6UEIgbPjxajJbxBFKlZGtEaj4zIM6uJtjUCjtULP1y3aF2+vc72FVWyXH3kxR7dwfWcCYCOxrdrrNbRSCMuh4WTQMoWw02C5CCeDsFyw0wjbjeuT8lypTN+XYGcQTnYh2Zn4OCcb77OsDfTYABCoiG/OTfL5mdN8c25yydof1xUrfM/oTl5S2U7JeWbW4bhUxCJBMyZ+ZcTiZpbz51u0W9BpQbsNnVbsjdluxQJnFAoadWjUYfxsQBx8JcZ1NfliLF4WCjrJY0HT8OwhJS1250rszpWW7dGIgs13x88z3m0x0Y9FzYlem8l+G09FNMjRsHLAzhXH5uiRU3UKqsGYfoSMboGO8IWkLkdoiFE6cghP5EGn0NpF4LIgZMaBfhSSCV8z4Xf4Rr2z5CxZKdiaTbM7n+OOazLkyBHqEkcnNcdr8fqX952D+84tTB8/NAqHKpp9Q5Ba96cZg8FgMBgMBoPB8GzgkoPuAERRdEU6ZXj6yd3x81Qqv7quqbCLI99eyHZezBQoRkZyzM400EqhtQIdLRI6o6ROIbSiXE5Tm2uilQIVDWz1ojIqQusIoSMKeYdmvY1WIahwsI9kW6ukrCMyrqTb6aCjIK6Lgvi4yE+Oj+t1FGCLiMDvx/sif0m+hKQ+8jur34hV6K/X0E7TSOXQThbh5BGppUku33bz+HIM5dng5GPv1H8B6CTC9+enT/OV6lla4cIzOlAa5sVD2/meyg42pXMXaOXZjW1DqRynmIUXZxRBt6NjQbMNoZ9iZjqg1YR+T+B5Am8GZmdg+ZqZxVKXbF5QKEKxpCkUoVAE+8LLexquIoQQVNJZri+Ncl1xdMk+pTVzfo/xXpuJXouJfouq6nO22WCy16GvQjpk6cgs03LlmtQ2ITnVZETPklWnsLUPIsTDoSvytGWZHiWUzqBJA2kENugkWnnikdlVmmPtHsfavaV9B8pFh4ocJqvK+P0crb4zmD7+6eMCR2oOjMCtuwJ2ZOLo49I4vhsMBoPBYDAYDM9qLjnoDkCr1TJBd54reBCeiuK52xdzaRQQti9uK4gDNwhhoXo2op4BvTwu8rJjBFhWFrvTvfgagiJZGzCVxUt1L95voFzOIurdixsmtvVVbLXWsZCqArQKIApB+eSzFs16MxYwwwWBUyt/UZ0HKsCWEX63gw77EPbRkbdQDvssv/iFe9ZG0x5c6mo/GQx6LAQilUuEzlySFoudOVrDFaLQRaSKiHQxnm5PEjh4Ocuf+1oPMln6NOpGS9cCEGuUJUS+WrlugFg7VyK2n+53+NrsOb46e47zXgdNLH/sSZV4YWU7Lx7dzs07tzE3145vaW+hf0vi2Sza1kqvayw9m7AsBkJj/IODS7XqozWEgabVgnYTWk1BuwWtZuyZGXtlahp1wSSw+MFlsppCCYpFKJQ0xRLk88+xG/cvACkEFTdLxc1yU3nTkh+klNI0Q4/Jfofz/Q7n++0ledXrEmLTkMM0WD1KjtQRWd0mwwwOHuiQUNj4IkNfZPHIoUiDTsTMwVqZ84KmoBYE1JgCpsACmXFIqyLpqEQ6KhGoFEdm4MiMD0gytubgCFxbiaeRm/UvDQaDwWAwGAyGZx+XHHQH4F3vehfvete71jzGBN15lqDB+h+C9mwfm/V55LVZv+1G7a+Wti9sawFLF/7zgTSb19U2wNUwITkCNrIU3EbuX2tDtr0NPfNmYr+NAm/iEG/i0AVsuxtqu7HIXgu9UuBclBpWB0uLhTq5il1S13S6WJGIt2UiCstlxy3K2+k+MlywX5z0orKwoJfzkb5InNb0IDYKctHSghKwY/tgOER043rHhiELhrJASS8JDt0PNVJmOXe2S7MuaDVjMdPrC3pdQa8L05MwL2R+iS7ptIiFzBIUy7GQWSg9O9fJ1FqjlY8Ke6jIg6hPS9v0ai2Uir3C5z3G9bwnuJ6vjwCFbmZptfpoZPwjjogfsJgPTIaIPaEF2EGBbtMHEa+7K6SNWFQm2ZaWhdZX3ltYCEHJSVNy0lxbGFmxP1AR014XL605Ol1lut9hqt9l2usw7XWZ9XooYdEWJdosn6a+gKPbuMxg4SOEJsQhII2v0ygyxH+lXIR2AEkkIzrSp2vPggZbZ0hHJdyoiBsV6YUWD03BQ1PxuLSskJG8x8ERzfM2W1wzZNyDDQaDwWAwGAyGq50NBd05cOAATz75JL7v83d/93fceuutJujOcwEB+gBILVDR+taelJZct+3AXq2zbbl+2432ZcO2G+iHZcl4DdBn0BYW3T+tlggoS9YaTeqF1gtT9Aes4Yq02O1yEOXdSsrzQZAWylIsun/LHe+WbUshUMs9G5eVldYopVFaJ967IDSJDCQGvRaruodeGkKLC3pbxo6m6zufQi+xvdhRIRFynW17BItsL35MBw9rHSJuCsDqc40j4tgqSVK2JhQQCvA1eAq8CDwlCKQglBBaUJOCaQtCCTKrSZcgMwTZEU1+FIqFK+uRqXVE5LeIvAah3yDyGkR+g9BbKEd+g7P4+P0OKuyjoj4q8lBhHx2te/GGZwQh4gBhQsaBwKR04qBgg+14fV1ppZjO5ghCC2GlkVYqyV2knUZYbly20kjbxQmH6Xc0QqaRdgZpx+vxLg8w5kiL7dkClUqBfbK4wis+0opZr8eU12G632Xa79Ai4FyzyYzXZcbr0otCApEmEGv9hOMDPrauYukAASgsQu2gyIJw8UkRyDlaTrxmZirxvnSjIq4qEEU20w2b6QbcewJC0Se0T5Jyu2wqBOwp2RzI59mZy7A545I2awcbDAaDwWAwGAzPOOsWLB3H4S1vectg2wTdeW6h3qgZrmTXvYbl0DptN2p/tbT93OhHPCU/Tqvbjgymfiq010T35lBJ0sty1a2iu1V0sL4p9VZ2GHJbkPkxrMIYsrA1KW9FZCtxFPdBn/MrrlFrzWOtWb4wfZovV8/SXrQu5b7iEC8d2cFLKzupuFlWVToT58iRkTyz1fZCbCe9LC2qE8DwcJ65C9kvsh0q56jNdQbxpkhysdg2aadUyNKsd2NbtWz/8uMV5HNp2s1+7AarVksCVHyuTMqh1w7QEYiI+Jj546KlSURgC4uwH8URq5anaJnoG4GIxJKFV+dHlQus389v5ZoATdEldARRKp4RLLJg5cEugMgBaY3OgMhAsCWKryETn1Q7mtCbI+icJ+iex++cH5RPeDN43Tkiv8UFFecNIKSDtDNYdgqt5wX7WKAXYvn2vIAvsB2LIAiTHwpAk/xwwLLgZlohJYSBj9bx2rt6fk1dnZSXoXWIDkPitQ4uTHMD13pq9TuAtBcETGlnEoEzw1S+RKhSSDuHtLNIJ4flZJF2joyTZZ+d40A6i1WssGnzZmpNxXzQp07oM+P1qHpdZvxunCfbc2Gf6V4bTylC4RKK5f7gfpLiWyp0hIUmkBLfSaF1GrRLOqrgRiOkozIpVcDWaewgDQFU23D+vMcXZQvfmsazWgjZo+habE6n2ZFLc1NjhDFhszWTIWsbMdNgMBgMBoPBYHg6uOS4mp/73OcYHl59zSqDwfDsQgiJSJchXcYa2ruGTby23fTEJKo9jerMoLozqM70ohTXEfaJunPQnSOaOcIKqUXayNxmZGFrLGZu2YNvjSKLO5hwinyxOs4Xpk8z5S0EMhpJZXjp6E5esWkXL9i94+IibjIlW1hiYUr0RW8EyIKIw8mvYw1VqyJjr8NltssPFQKcioWurliedM223YpDq9q/gP1ClPBMxaWTrEl50aYFDFUya98/DSgNYSxuDhdyzE110D6IAAiINaKkLOa3Q8jaLr2aH9+/ZUn3QHsgfZAqFixtDbYv4uPba9yIhM6yJQaU8MDRCDuNtIewbAvHzhM5o4R2g5RTI7TrhM4cUdZH5CSWW8RyS9ipEpZbwkqVsN0i5ZFROl2NkG7cnpWOxTlrfttFCGvdAccW3+v12l/MVg8ClEWgQ4bKLrPVGir0UZGPVgE68lHKR0cBWsVr6PpRn3bURaQi5loN+mEfL/LoKQ8v8vGUT18FeDrEUyG+DglQBCok0hGRVigEkRCx/i1Esh2iaKNUB5rVZU9rUVnr5UvWYmmNLSS2tLGFjSMdLOngSAfHSpGyXHZZLtcV8oisi7TyRDKPL9J42qYbatphRCPwqPs96kFAX4EWFuHgTCHxoGrTt2fpa2ggkZGDG23CjTbjRhVSqoitXezIJRdVAIgI8PotzrSbHJ1r8anxo4MLsoQmZ0s2pVPsyGbYm89xsFhgZzZD3jGhyg0Gg8FgMBgMhsvFJX+63rZtG77vc8899/CZz3yGc+fO8fu///tkMhk+/vGP87a3vY2RkZVrXhkMhmc3MpVHDOXXFDa11hC0KNot5s4eI2pNErUmUK1JVHsS1T4PKkS1xlGtcUJg+liGr+f389XCQU6kF9YBTaO4M+3wsuExbtpyDXZmGGnC/15ZFjvmCpDDcuD5uZa+CfPCaYpO1bugOBcBUaQRPuRklvGjXbqz4M0pvLmIqGNhBTapCJyIOA/j3A01qQgsLZDaJeVvAn/Tui5LCx27gxZA54F8khc0eS+NJfuoPJBN0vqXPX1aEELgacVc0GI2qBPqgInmLK2gSyvs0o66tMIO7bCX5HF9T3kXaZhFTtiS5WvzPj0oYlXbW/D0hUURxC6CBISFJEWKHCmRRYoMAhelHUJlxQkHZXv07LP0OAuA0BapsEI6HMWNNpGKRrBwyEbDZKPhpHchvmzjW2182aatOjQDj2Mtjy9M1Rd1Q5OxBZtzLlvdNPvyWa4tFtmdyxox02AwGAwGg8Fg2CCX/Ana8zx+8id/kgcffBDXdfF9nyAIqFarfOhDH+Lee+/lwx/+sBEtDYZ/YQghEG6RdGUbKWvbCvFKqwjdrdKon+W+uQm+2mnzcGShEhcmqRU3dM/yotYT3No5hatj9aINCCeHLO0g3LyfILMdq7wHa2gPIju6Yn09w9VLFLXoNb5Lxz9Ls32Efu84fnQOShpdhIBhGnoPnt6Lr/cSyGvpRztQOhUHWVGQCsGNwA0hrTQlB/JWrDUWpIXoRFhdoA2iK+Jp7u1ke0lvBD18LORAt9NSx8JmkTgvAEWgqAm2h3EDBSDPuoVNrXU8tVuHaBWi5qd96wgVBdTDJse7fU7NTjEXtGNhMmxTC9vUwi5zYYeO8i9+ojVICUkKSQqRlAUpIeIcgTMog2tLdKiwdCzCJdp14qiskRosNFLreNq7LQnDeO2DpS93saROi3hNXi8MCbUm0ooIRaQ0ISre1ooQhdKKEI2vFD6aAE0gBAGSQAh8IQnmk5QgIhQ9+vTi1QsWd2Q+SJVOBFntIpJAPmiXfmqavjMf2MclFQ2RDjfhRqO44SgSh7Qqk1blQZOB6OBbLTzZxpcdQtlHCUEnhBMNjxN43DvdACaTLmgyNoy4DjtzWfbl8xwuFXh+KXPJz9RgMBgMBoPBYHguc8mC5R//8R/z0EMP8Vu/9Vu89KUv5cUvfjEAd911F7/+67/OO9/5Tt7//vfzq7/6q5etswaD4dlNNwz4xtwEX66e5du184RaMf9n6GB+mJeN7uTFuRzF3hSqcZCocYaoeRbVOItqn0cHHaLq4zSrjy9t2MlhlXdjDe2JRcykLDNDT/9FGlYQ9GfpVh+hO/MQnerDeI0TrOavablDpEv7SJf3Jfl+UoUdWJbDyEieM6fbNOrQrEOzIWjWYboJSi0XqxWkBM4mTaEEpYJmyNUULcijsb0AWgG6GSFa4PRtoppAdhysXgqhBDRANJb3UNDBWxRFPsJ3m/jpGr5bw0vP4qWq9FNVvNQ0/dQ0XmoKXzbQRHhCUpMp5iyXOculZrnMWfG2L9e3NqKtFXkVkFMhGRWR1iFprciokLSOSKsozpNyRke4OrraHEYvKxoIEPjCwpMST1h4wsJH4ksLbz4XFr6I9/eFRVda9KRFV9qEIrlDWtKzU/RS6Vi+1WlS4XbcaAduOIYbbcFRRRydwwlzgzVcFSG+VceTTXyrRSB6RCKM1zRFoojFzE4YcqbT5N7pZFXR7zyBRJGxBSOuza5cjgOFAodLBXbncrjWc/nJGQwGg8FgMBgMa3PJguUnP/lJ7r77bn7oh36IWq02qBdC8GM/9mMcOXKEL37xi0awNBj+hdOPQr5Vm+TLM2f5Zm0SXy1EJN+VLfKS0R3cfeAQOU8ueGOWt8HYLUva0aGHak2gmmdw/Ula408Q1k+iGmch6BDNPEo08+iSY4Rbor9pP6qwB2t4f5xKuxCWc6Uv+18sWmv8znm61YfpzDxEt/owfvvcCrtUfgflsesRmV2ki7FIaafXXhdZCEEuD9kcbNmqiaIugd/A6zdoNTwadWi3bDrtLF6/TL9fJggs5qowV4WTSyK0d5HWSSx5Cit3Eqt4CmvLaYSYQWqblD+M648sSsOk5steBdev4PpDCG3h9odw+xcWxj3Lo5quM5NtkEnXSWXq2Jk6MlNHp+tEmQZzbp2sCCholSRNUWuKAopaUBSCEpKMsJDSJZUpEIYMAvzIQaCfOAiQSIL/zG9nsmm8frgQJAiZ2MmF4xAIKcnlMnS7PiBBiCS6vSR2Yl5UJ+K6fD5Nu9VPJOj5oFeLI1rF/ws02VyKdqubeJuqZXmEVnG0KK0jXFfS7XRQKkAl63PGKd4Oo5CeEnjapq8sAqUIogClJRYWLhZOZJHBQmGhhERhobHQCCQhEoXUEZoojnCPJpARPh0C2cETAp9JfPl1+m5ERyp6uojQ1+CGe0mHu3DDbUjSpKMK6ajC/KK9kfAIZA3fauDLVuyJKUIENvPrLsRipkzEzIgznSZfmV4IkSSFImsJKq7D7nyWW7aOssty2ZHN4EgjZhoMBoPBYDAYLj+1Wo2f+Zm38va3v4Nbbnk+AI8++l1+//ffw6lTJyiXh/jJn/wpvv/7X39F+3HJguXExARvfetb19x/44038nd/93eX2rzBYHgWE6iIL0+e5h+OP8k3Zifoq4VQGFvTeb5ndAcvqexkd64UBxspFKh6rQu2KWwXa2gP9vAeRioFdBKcREcBqnmWqH6KqHaSqH6SqHYS1ZpAew16Zx8AHlhoSDqxB+a8gDm8H3t4HyKVv0J347lP2K/RnvoG0w8+RO3cAwS96WUWgnRpL9nRm8hWbiRXuREnM7wiyIxSIb43h9ev4vereP0ZvCRHt+i0qwR+g8BvoNaYHi2BTArSjoNSO4jUHqJod5yr3Si1DU2BKLqRKLpxaS9Fj1RqhiBdJSrVUJk6ZFqIjEdkzxLYHUShRr33BJIUVi+HbqepNRWthiZoSWjZZDtZRvolKr0yhSCLG7ls62xmW2fzqn2GeI1NWZSovEKXkunoRY0uASXivAhkQMhnLvjPlWo7VJqaHzHrRcz2I2b9EM+yqYZ9OqGiKxTtUNFVmq5SdCJFP1rUyPy07yu1VKRKEoBW2Ek0qcCqEslxpLaxdAGhC1iqhNRFLO1iRVtIR1sWNePjW3V8aw5f1glkg0B6xNG7HIS2k4uQsZipJe0Q2mHIqU6TL07Ni5kaKTS5ZJr5nnyOa4tFriuW2J7LYJslMgwGg8FgMBgMl8DDDz/Ib/3W/8v4+ILTSbPZ5Fd+5T/xb//tf+Duu9/AQw99h//n//kv7N27n8OHr79ifbnkj/aFQoHZ2dk19585c4ZCoXCpzRsMhmcZ7dDnW3OTfH1ugm/VJulFCyLlJjfLSyo7eOnoTvbmypd1vUlhOVhDe+MgQHsW6nXYRzXOkAnGqZ/5LtHsMaK5Y/G08rmjRHNHl7Qj82NYIwcQO67Hz+7GGj6ITJcvWz+fS2it8RonaE3eR2vya/TmHmPJFG9hkRm6hmzlJnKjN5IduR7p5AmDJr3uBLNzD9LvTnLmaJ16bWIgTPpejQVV6OJImcJJlXBS5SQvkXLLlMqj+L6DZeew7RyWncV2cti2BlGj38/Tbrq0moJWA7odSaOu0DqD5+3E83bSWnIeTb4A+aLGHuoxGZ3lDCd4XN3P8eAUKqMgAyzoUmxxRziQ38kNxT1saY6yzdvEaH+IdDsFDYFosjDtvAlCCXRDIxoCMT64kSvvva2hBK2RHjIrkvU19UDQnF93k/S6b+PTQiuIONcN6LYjTlXbzPYjqvPipBdR96NVgzqthxSanI5IEa+Ba+vYh9LWGktrLB37Vdoq3ra1QggRx/cRklDEUdCjReUQEZfRhELQQ+JhoUXsWRqQISBZf3LFY2qAbuDoFI5KJbmLo+NVRNPRJtLRQrAojSIQXQLZIZBtAqtFIBqEogtSgJYILGJRc94zU6C0oBXE9/ZUu8kXzjeBc4DGEoqcIxlO2ezKZbm2UOJwqcjOXAbbeGYaDAaDwWAwPO1orekv+o58IWwhCaP1fy9aC1daG/ru/U//9I/8yZ/8MW972y/w67++MFv6S1/6PMViiR/6oR8G4NZbb+NVr3o1H//4X12dguUdd9zBX/3VX/ETP/ETK/adPXuWv/iLv+BFL3rRU+qcwWC4upnxunx9dpyvz03wSGOaaJHb1KZ0lheObOcllR1ckx9+2oPiCDuNXTlIqXIrwdjLY29MrVHt80Rzx5Yk1ZlKIphPMnv6y4M2ZG4zVuUarJFrsCvXYI0cRLrFp/U6rhZU5NGZ/g6tyftoT963wosyXT5AZfcLCZwt6FQOz5ul3Z1kZvwT9I/+Cb3uBFF48bDPQlik3BHcdAU3PRrnmQojlW14fhrbSYTJVBlppVeMq/V4/GVzMDwCoBP7LNNTLVotTasBrSa0GoJmMy6rSNBsxOtmcjYHXMsY1zLGa7lDtmi7VWS2T7EIY0NZ9lVG2DKUxbZX9mW10DQoEF1NWWZpnO6iG8TC5byoOZ93BSIUMAvRrEIOlLJVhE1XLwiYReht9hA2sZhZXBRIKL3q4ZdM04840wk4m6T5ct2/+AcuG82IDhkNfSpej5F+l7zXJx8G5KKQXBSQC0Py0fx2XOesxxX0MqAAL1n3smfZ9JO8Z8XrYHYti44taDiCWkpQd20ajk1bpujYaRoiS6DL2DqzRMyUSFI6TyrKQ7R5MKVcoQhFQCB9AtEnlPE09YgeyAi94PK5qJcSIQSRtmj68fM41W7xpanWwFYKRdqCcspmRzbDoVKJO9lCJRSkrPWtpWowGAwGg8FgWD9aa/7zt+/nSKP+tJ73cLHM79x027q/i7/gBXfwvd/7amzbXiJYnjx5nH379i2x3b17D//4j39/Wfu7nEsWLH/+53+eN77xjdx999289KUvRQjB5z73OT73uc/x8Y9/nCiK+Jmf+ZnL2VeDwfAMo7XmZKfOfdVx7pud4HintmT/zmyRO4a3cmdlG3fu3sXcbHtd00qfLoQQWIUxrMIY7HrJoF55TaK540RzR7HbJ+iMP4pqno2FzM4UwWIRMz+GNXIQu3IN3X03o5wdCOe5OZ086M0yceQzTB79Iu2pB9BRH53IJJHtYBd2QKZEiKLZm+LM4/8fqwXTWUzKrZDJjpHObmF4ZAeRLpJyRwcCZcotx+syLmKj05QvBWlBsQSZQki1dYKHCkf4VuMIp8qT5MMhhoItlP0tjEbb2RRtJ++PIP0MGVUg0ytAD5iF6kmoAqDJ5mBouEfKFeTymlwecgXI5cFZvIyqBApgVyx0Ll7+Ua92HwMNrVjALKoMrfHegrdmk4XcEwhPwAyImfhQjxBrldA72tYLAmYiYva3+AiLhQjp8xHRFz2WSGlOtgOONj2mTrU4Wu1ythPQCNYWJjfpkG2RR6XfZbTTZrTXoeL3GPX7VPw+5dBfMziQlhLtpsBNoXMuOl1Auym06+KnXbSbIlNMU+916EsfT/j4wqcvfHx8POnj4eHh49NHpqDX76F0hFJRnOuISIeDstIRigghNTpQCAVSC2QEUmmkYqEcgYw0MtJUIsFYYOF4Fo6ySSkLR8XradrKIiJNX2RoO5I5R3PeHaaa2kzN2kRHjuIzhKKQCJkuqchNHsIoEAuZkQgJRRAnmeQiRIkACEGEySt1Pmm0AJFEYO9qTTfUTHS7fKPa5U+PTyZ3WpGSsXfmqOuwO5fnULHEoVKBsUwGaaaaGwwGg8FgMDxnGRmprFrf7XZJpzNL6tLpNL3exR1SngqXLFju2bOH//t//y+/+qu/ykc+8hEA/vzP/xyAbdu28c53vpNrr7328vTSYDA8Y/SjkEebVb5dm+T+B84z0V2YLCuAQ8UKdwxv5Y6RbWzLxMtACMGz6outdIvIsZtJbb15IIwpr004e5Ro9knC2SeIqk+gWuMDT8zg9Jc49wCAQJZ2YleuxRo9FOdD+561gX1Cr07z3JeonfksrblHiIQmEhBKUI5NKDRaR0AAvROxULcIy8qQzm5NRMkxMtmtZLJbSWe3kM6OYVku8PSIkOtlslvlU+fv51v1x3iw8QQ95Q32CSHYPlTk+eXd3FY+zJ27r6M220FrCAJFuwXtJnHeErSb0GpBGAi6Heh2FPErZenrIeXGAmY+D9k85Asavx8RhJDOwKqzdh1gGBiBVMVGV+MfEVbg6aUCZhOyQYredBBvt5L6fuKxOQdibuHwPsGq4maQ0bQzEVUnYsIKqKYiam5EIxXhuILhlIV0BemUx86gy65Wgz2NWXb12uzstckuCrg1j045qGIevXkzYTGHLuTRxTjltw8z0Z6jJbq0dYtOUE9SLU5+nXZQoxPU6QZ1vMYV+sA0/wjnBdsrtE6mowVbQgc3snHDNEQ78dROOmIvbbGHntiOzwgSC6lj70wAFt3WiIhQBEQyFjBjYTPOFdFgGGqi5ECFFouETaHwI4UfRdT6EU82G3x6ssH8UQKFa2nKaZthx2ZnNss1hRLXFItszWawnkV/9w0Gg8FgMBieLoQQ/N4tt9Pxg3XZ27YkDJ/+KeFrkU5naLeXxpzo9/tks9mn3PaFeEofu2+88Ub+8R//kSeeeIKTJ0+ilGL79u1cf/31SLNGksHwrCT2omzwnfp5vl2f4tHGDIFe+GOZkhbPK2/mzuGtvGB4K+XUVbZY3mVCpPI4YzfjjN08qFNeK17/cvZJourj6NpRgsY4qnEav3Eajn8qNpQO1sgB7MohrMq1OKOH0CNX5w84SoW0608we/bT1Kfvp9edIBQQCcBdbh2Cjqdtp7NbyeZ3kM3tJJvfSS6/g+07D9Fq21zWOcZXiDm/yeeq9/PZmW9wuje5ZF/ZKfD80iGeXz7MLaVrKSYetEKAJRbe2xwHhobjFJNExNbg9TWdNgjSnJ/06LSg3YZOG3xPJAlqg6WgBQ/QJ47Crclk4qnrC0mTycbl3MUcel1gFPTooGkyFZdO1V8qDgd6qYDZAtESuJ5NazrAr2tkGzI9iaUFTk8w1LMZwubAysGxCA2ih5BdhOiB66Pz4I1IUmNZ+llQlRRBxaGRr1OPzlPrT1Lvn6DuTVLrn6c+fZ7WuerqnqYXQWhIIXGUIKXAUYqU0jiKZBtsncTp0UlKygKwdNzG4joNaBHnKimrxXVJriF+/cg4D0Sch8n28nIgwU/0bCU0Xcen6/hAF5gDHgQglSStLSK1lUjtWJGUHsXCwtIWRCv/LmuiRMCMlgiZ0UDQVCteujoRNefFTIjoK8X5UHEexZFam3+mCSL2ChYoLKHI2DCcctiazXDDphG2WS57cnmGUs7TvjyIwWAwGAwGw9WAEIK0tT4JzrYkoX7qguXlYu/efXzzm19fUnfq1En27t23xhGXh8viJ3Dw4EFGR0dJpVLk88/NqZEGw3OZOb/Hg/VpvlOLRcp60F+yv5LKcMvQFl65ey8HrBKuvFKheK9upFtAjt2CM3bLwENw+uwZwpnHCauPE1Yfi4VMr0k0c4Ro5sjg2Fa6iDVyDVblMPboYazKtch06Wnru9aKfvc8ndZxpscnmJ58lGbtMfr9GZZM41407deyc5SH95Nyt5JJhMlsfgeZ7FakXOpBKgSkMwXanWfeY3ItQhXxzfqjfGrmPu6vPRoLNMQi5LX5PTy/fIjnl69jX3YbUlz6j27xvYBMFioVh5FN/SX3JAhiMbPTigXMTlvQaUO/J2m3FEoJel3odWF2ZtDqknOkMx3ctCCdjs8Tn08PzpvOgOvGfVkTBxgBPQK9UPGduT73V3t8t+4xk19YEFxoKASSA77gprbHNTMddrUCyr4NKgsqg9ZZtM6CShbF1Fl0lI1HVgh0gCnwnlhwVrSANHmKdokRdzNtt0bbnaOdqtNxa7TdGp1UncBpoOw6yprDkk0ySpGJIJuk+bKbCJKWBjGQD5fdP2EhUjmsVAaFBdJBSBssB6SNkEluLeRuOo3nBbESPRClk/LgwWpIPlSmbPB6PYh8tAogDOI88tFRsFAfBaACNJpAgC/Bk3G+IgnwLPBkRF+exbPO0pfQt+N6X4LSmWUi5laUGiNSYyg9isDC1hb2Gq9PTYgWPiFR4lmtEzEzSlLspSnmFdwlxypEImwqoehEEW1Pcaal+Pr0NPOipyZCCIUUEY6EvCPY5Lpsy2bZly9yTaFIrpxZ2TmDwWAwGAwGwzPGS1/6cv7wD9/HX/7lh3nDG36Yhx9+kE9/+p/57d/+vSt63qekOtRqNd7znvfw6U9/mk6nA0A+n+euu+7il3/5lxkeHr5ICwaD4ZmgHfo83prl6PnH+NrEWU506kv2u9LixtImbhnazM3lLWzPFJBSXDVTeK8mZGYIZ8edODvuBJLAPq0JourjhDNHCKuPE809ieo3UePfJBj/5sKxhW2xeDl6KM4v01TyMOjQbp2g0zxOu3mcdus4neYJoqi3qr3Q4EiXbH4X5U0voFR5HrnCHtz0CKOjxWf9Mz/dneTTM/fxueo3qQcLUxmuze/mrk138voDL8FvqqftGh0HykNxilkI/jMz06LX0/Q6JFPKodtJppd34+0wEPR70O8JGktaXhaASMaemukMFEt9hBS4LrhpjZuOxa8nun2+0+ry7XqPxTELLWCfDLmu2+CGqXFumJtmJFiYKq8zNtGBMaKdY+QPlphzLeasGaY6p6hXJ+nM1ejX20TNgGy/SN4bJu8NDVLOj3NL22TCApmwQKWz46L3TqPQdgvltNBuD5320NkAsprUsMSzQ3ROQMGCggMFB5HNIpwsws6AldrQ37KNLl2wEXshYGQ4S3WqivJ76LCHDnoQ9gdlHfbQYR+CHjrskrYCus0a2u/EKeig/TaR36YfdeiLJ+nLJ/Ek9C3oJXnXsqmLTbTkGG0xRldswWOMQI+h1BhKVxDYCG2Tim/02vcfDyXCWNAEIqFRIooTsbiphFqYgr6KuKlR+CjmfMVcV/F4TfFZaiBm4YHjsbBJiCDCshQpqck7korrsj2TY2cux/5CkVsKbiIeG69Ng8FgMBgMhitFqVTm93//f/Pe9/4uf/In76dcLvOLv/hfuOWW51/R816yYNlsNvnRH/1RTp8+za5du7j99tuJoogTJ07wsY99jG984xv8zd/8jfG4NBiuAqpel0ebVY40qzzarHKqU1/xfXRfbmggUB4ujuBIEy32UhBCYBW3YRW3kdr7yrhShxTUeapHv0U48xjhzJE4qE9rHL81Dic+E9tJJw7oM3qI1N5biNJ7ELkta06h1Dqi2xmn15pgcvzRWJxsHqffO79653Q8FdbWYClIuyMMbf0eKru/n0x5/yrXcjnuyDNDJ+zxxeoDfHrm6zzePjWoH3IKvLJyO6/adAc7M1sQAoqpHFVaazf2NCIEZDJxGh6seb301RoEGtfJMTnRodeLPTF7PUG/C70e9Hvg9QVaiYHoOVeNWBB15nMJOByiwDVofBnhCJ+S32LIa5GNeqSUxo2GmcsWaQ1nsDeX0FuL1MoNpoLHmOh8itnxE0y2ThHqZWvyJBHK7QgqvmDUk5QDQTGEQqAphZD181jREJIxLGsHltiKpTchoxFSeoiol0F4aUTXQfQtBBIRlpBhacX6qRAHPV+OdjXkQOeAJPVGPIQEkQGdBbILORkue/T0tRDSQjhZpH3x9X8uJoZqrWPvTb8NYYdiRlGbnkJ7bbTfRvkttN9Gey20fxTtf4cwaNELGrQij6rKMmePUJebaFgV2nITXVGhxygeowQkoiYZZPJ35ELo2Hc08czUKAFKxGLlvKAZ5yoRPFUyLX1pw5HS9FD0PMVMW/GY0MRT5tvoB8aJ1+GMEMm6nFJEWDIiJRV5R1J2HLZmslw7OkxJ24xlsmx2M2Tty7Omk8FgMBgMBsNzkXvv/daS7WuvPcwf/dEHntY+XLJg+Ud/9EecOXOG3/zN3+RNb3rTkn0f+9jH+LVf+zXe//7385//839+yp28XMzOzvJrv/Zr3H///ViWxQ/8wA/w9re/Hdv+lzm91fDcRGnNmU6D7zaqA5FyyuussNuaznPLpjEOZ0Z4XnkTJee5uRbl1YCQNulNh0jb29HXvh5IIpPPe2HOPEZUfSyZSv4o0cyjnD/ysfjY9BB25VoY2YeXK9GTik73bOI5eQIV9Vc9Z8odwbXz0G8gevVYpNTgpEcZu+Y1pEZfjFs6+Jz7wn6sc5b3nf0Kn534Jp6KBTSJ5Pah63nV6B3cVr4O+1kuxqdSMFKR8fqJA21nqcijIk2/D92u5uhMyOmW4nTVR/mCXCgohZpCqCmFmnSkSKkQZ5AiLA22crG0g6UzWEohzkVwtoutW4wpxXa9hRfoUYR+CQKF1AqJRmiN1BrJ/FqQGx1jfWAcGEdLAVJAVkBBoIVMlPT5JJMFJQVCWKhQgJLJIpIStAQkui2hbcXHaIsQgaOt+Hgs0BKd5GChhYC0hc5IWsUm0gEyAp0RsaCZ0eh5YTMTJ50mFj5Lz4w7shACbBdhuwgxQqZSoJPafVFPz+FECJ2ZaaC8DtpvJaLmvMB5HO09SOi1aXmKmidoaJsZT9KIXJo6R5sCXTlETwzRZ4hAlGNxmRRyPjjQOm+LjiVMtNBJrhbleiB4LuQaLQZ7Bsf10HQ9xTSaJ2uKL0704k6IOvNyajxVfWGdTiEihAixhcKxwRGatKXJ2xZl12Ek5bLZzTKWzrEpm2XUSVNyn50B1gwGg8FgMBiudi5ZqfvMZz7D3XffvUKsBHjjG9/IAw88wKc//emrSrD8xV/8RTZv3sxXvvIVqtUqP/uzP8uf/umf8u/+3b97prtmMFwSWmtm/R5H2zWOt2sc69R4ojVHc9HUTYgFg735MtcVKxxO0oibMVO8n0GkW0RuewHOthcA81PJxwmmH6U3/W269SM02+foWXP0Ol/D739t9XZkitLQftK5PeRyu5FBF2/mu/Smvw06Dv0sZIrC9pcwtOvV5Lfcyuho+Tn33E90zvHn5z7J12oPD+p2ZrZw1+idvKJyG0Op4jPYu8uP1hr8CNENEF0/yQNEL4BugNfyaM31aTc9dnkhhyNFNlLkQ4XztDz4yyOEC6VBLfT3Yq0ukaIvx2+R/TjZgNbzAmksdC4WOBcLnh0sXCHBipO2LXAk2olzUhKdkpC26JW7WCpEpQS4FqQl2hWQsdBpARkJrogj7lxhXUwIiXQL4BagsLpNHti6iqen1hrCHtprofxZIu8Ura5Hux8QYDHb9Gj50AygGUnakUNHu/R0lh55PAoEFNDCRsRx0BcEzqcwXBMJcyBoDv4t3har14MmEBofTQPNucH+WOSMa2uDKOtK6IUgRWJeDF1IggiRiKJSaCQKS2psobElpCxB2rLI2BZ526bg2JRSDttnS0hfUbJcyimXQsolbztkLAvLBLg0GAwGg8HwHOaSP86fP3+e5z3veWvuv+mmm/jEJz5xqc1fdk6fPs3999/Pl7/8ZTKZDDt27OBtb3sb73nPe4xgaXhWoLVm2utyrF3jWLvG8U6cN5aJkxCvQXltYYTDxQrXFStcUxghaxsvkKsNFfl02qdoN4/FHpONY7Rbxwnn11lcNkvU0ZJ0oMiExCmCVOQT1Y7Ry56nrT+LUv7APjN8mPLuV1Pa/nKsVKxAPMccKjnZHedD5/6Je+ceBEAgeNW223nt0Is5mNv1rPIg1VqDFyJaPqLjI9oesu0j2vPbC3m/65ML1o4c6BLPyL7g+QhRMsB3PDp2l5bTo+l4eJaPbwUEMiSw4im9KR2QRVO0c5TTI+TyY+jcZqLsZrKVbdRbikAJwkgQRIJQCYJQEkSCQAmCME4IC68fEYWgIghDjQpjMVAsVqaSwDmCxFtzhfdmXLewrZBaY2mFpWNvT0upZDtK9kVYg3x+f4SdHGOppDyfJ3aDifRCAxGIKN5ez0NVgJ+kNbiYb7tOvERjMVSSEYLEfxUtZHyXhExSUpaSORl74TJfL0WcW3KhLAXaEkykbEKlBnXIRbkFWsqkCwIsQS/bpO+HIOLjhST2SrUyIDJouQkpoWQJsgWXEcePByUCLRNv0OSykCKJkt4nCDwcRzDb7BAGEf0oxAsVfqTphRG9SNNT0FfgKQsfBx+LUDsEpIiESyBcFn+8TUJrzQ+rhVGWOOkurtPJU72QRrrWq04v+luzIHoyED9jYXTR9vxeAST7+mh6wNyi/YokVP2gzRAIV7QftzE7qGVRH+bPv2CvlvRBDPYvHDdod3kSy+vittBxlPj5Kf0iiSwvVrQR74tT/FoGEEINAlsJEdeLRUkSr/WbjPzkeLGwD5L9yd8HBFKA7Qh0qAae3kJoBCK2ERqJRAqBJRSu6xAGCpncF0sKhBALbQuBJWL7VEoSBQoNWIhY8CcZVlJgJ8dZSQC3fMal54VoDZaI2xPEdnHHLCwh42VBzjj4fvwE5KI25n+QkUIgk14iYKRRpNnoDgbu/Ei0ktkEg1MAw70yrVpvMDJEYm3ZcunBAuZ6WXp9b8XrYVWZXIBvV2g0Wxf/kUGAb3v02v661vq10uD7F7edtzcYDAbDc4dLFixLpRITExNr7j937txVtX7l0aNHKZfLbN68eVC3b98+JiYmaDabFIvr875pt1sIIVEqSr4IC7RWCCEBjdYaKS2Uir/QrFaeP06pCCmtDR+31vm01ti2QxSFqxzHinNYlk0YBvGae5ZFs6lpt9sXvaYF21Y8Be8i16R1RCql6Xa7RNHFr2mx7cWuCTSZjKTdbsYfAhdd0/JzgCKdFrTbrYveb60jXBc6nTZRdPHnFK87F/dj/lvQ2vdQk81agz6v9pwCrTnTrnOu32LuZMgj5yc40W3QViEotfCJTGukkOzIFtiTK3GgMMLNW8YYUQ6O5Qz6ie/T7veWPCfLsnBdaLebg+d4obEnhCCV0nQ6HbS+8NhbOp4u39hbbTxdzrG3eDwpdXnHHtQ5c+oh2s0TdNsnadWP0+2cQ4gIlXwDljJ+vFJYFMu7SWV2UiztJ1vYQy6/h3RmmLA7R1h9grD6KPXJr9PuTqDoo9r9+AuVhnQAOVxcC8Jzx2l5DqK8D5HbRCqVWuP1u/I52bZNo6Fot9srXuvLn9Nqfxcu9ndvrWeznrF3zpvhw2f/mftmH0LYEiLNC4dv4l/vvIsbxvYyN9em221fcOwtf60rdfnG3vK/C9qLsLohqtFDdnxkK4BWD6sdQNuDlsdsNyLyA7TWONIm0EmfhSTSavDFUqMHdVoKRM6lKjRTQtC0HeoWtB2bTM5iZzDNvrkjaD1DYDcIUnOMZxqcyQaM5zWTKYgEseCk4uFaVg6bdZmt2V1sKxxiy9BNpIf3xt8ak3vRX/R+4JSzWHNtLOJ1Vdd6f3Ich1Ipw9xcZ8XYi0KNxsL3IsIQpLBJpRwajT5aWwRBhFYCpQRhqNBaoiJJGGrQNkEY4aQcPE8RhFESsNsiDJPjEERBhMAiUhohBFEk4vcZBQKLMIqSL8QWURglzxaEinAAqUIspXAEiCjAUiGusBChh9SKlAAZhTiRwlEKJwpJIbCiAEspXAQ68rC1jp0ldYCl5wWbCAsNOgKhBs938fNfPhYEEOnYVi8bF/O2atlxMjlOJYLO4uMsrQZjL1zH2EsBtrAIVBj/LV/Wt8XncFbp24WuqXKBa1rruNWuKdrgNbGBa9pI3y72nC50TQKB1lfomkS8jIwUK89xwb4JiJTCknLDx611vo1fk73omqyLnMNeR9+sdV5TlJTDDV5T56p5TiMbeE6lDT6noQ1cU26d1xQKGf9Atc6xd34DY+/8BsZeqBXnk2sKL/KclIbzG3hOkxt4PYVaMbWBsXd+g38jZjYw9mY2MPZWs73Qc5rawNib2sDraXqDr6fzV2jshVoxmVxT9AyOvVAppq/Q2Fs8Rp7Jsbfas7lcYw9W3m9bWIRFif6B3RA20fOSm4BAsyQgoRAMfoxZXg60oO4KisO55IDL+YtM/Fl4vZOu4r5tvA9RFKGUottt4zjuiu+SlcoaU3oWccmC5e23386HP/xhfvAHf5A9e/Ys2Xf8+HE+8pGP8JKXvORSm7/sdDodMpnMkrr57W63u27Bcng4j2VZgy+rQgiUUkgpB1+k5/cDq5bnj4uiWDTa6HFrnW/+C2EYhiuOA1acw7ZtgmBBNIqiiJGRwrqvaXi4sKFrGhkprPua5m3Xc03xcymseU2LzzHfj/Xc78V9Xs9zmu/Hep7TvG3L9zjbbXGyXeN0p8GZVoNTrTpTXo+BimVJdBhfh23Z7C6WuaY8woHyCAcLw+wbGsaV1iWPvcXPcT1jb/7+rWfsjYwUrsjYW/xsLvfYm382lzr2tA7ptM5Rqx2lWT9Gs3GC+uwxfG82eeNcECYB3FSRfHkfpaH9DI8cJF/aS6m8B9t2V1yTlILZ3mPMzn2VuXNfJgp8kGBJh/LwAQruCE67Tn/mSQg9wqlHCKceIUjOJ9JF8mOHCEb24Y4eILv5GmRpDGnZF3xOq73W13pOa42ntZ7Tas/mQmPvZHOCPz36D3x++tvo5Ca+bMut/MTu17CvuO2Sxt78GLmUsQcQtfrIto+q91C1HrLlE9Y66HqffssnU+9BP7io+IFI/FbSNqKYReYdyKewSllEzkYW0oiCC3mHMJfha7Uu/3iuwXfrIToKAUHZCnih923unPsMm/pnUVpzfBTOpuFcGiazC85+8yJlwcpxoHyY/aMv4JrtL6OS37rh96eLvX4Xv56Wj6cLPactW9c/9jbyd2+148IwRPmgQ4nfj1A+KF8Q9EJ0KAk9TegpdGgReCGRrxHKJuiHRIFGRHG9jgQ6hH6k8JRFFEToCCQ2YZSI5cJC6SgeCWLRDwBao+fFLRXE04Y1oMLY6xMJUYCNRmrQKsBBgI4QWuMIUFGERGEL4g+GaCwkmmDgAYYOky8BsVebLWJRDa3QWEQ6BDRKiKSfsVebJv7BLP5QruPrUCFC6FiE1iECBm1rsdhfT8bnG5TjcRiXkw/BQnMJn4ef04jBf/NeeWuXF9+2FfsXJxYlsfFzPN3HmWsy12SuyVyTuSZzTc+2a1rYuZBpsZAv37+ijEZoiW3HThELO+fFxvkZCquVF2wv9bjLcQ6tLaSUlMs5MpnMis/l6+GSBcv/+B//I5///Od5/etfz913382+ffsQQnD06FH+/u//HiEEb3vb2y61+ctONpul11saUnR+O5fLrbudIBD4fuKGAiw8oIXJFbE31fz+tcrxRI8guJTj1jqfIAiiNY5beQ7fj5if2CGEYmSkyOxsa9EAXL1vi21X3ouV1ySEYGSkkLR94WsSgkX9uPg1CSEoFnOJ/dJrWn6OuB/5RW2vfb+X9vniz0kIlvRj8TW1Q5+pfofJfofz/TZT/Q7TYY/jjTnm/FUCpgiw0i5lx2VHtsjB4RG22Tn25YbYnSutjN4dgJccuPazWfmcwlAPrnFt28XPXS96NsvvxdLntHQ8rbxvlzr21h5PT33sxX1ZPJ4uPvZSTp/xM7HXZDyt+wSd1im0Dpc8IikgnbbI5LaRL+4nX9xPobiPfOkAbnqU5dOWowjK5XisBoEm6M1SP/XP1E5+Ar8Te7anbUiPHGRo7/ex93mvp9lZJACokKh+hmj2CcLq44TVx4nmjoPqoCe/RTj5LUKgA2CnsYb2Yg/vxxrejz2yH2toL8JOEwQRIyOlNV7rS5/TRsZeEKz2Wl9uu3Tstd0u/+uRj/GF6rdiuSNt86Lhm3jL9teyN7cNAN/f2NhbfYwsGkM6gm6AbHpEzT6y6ZENNP2pJrLhIZp9RNPDDldOEnWXbDmQctCORBdcdNFFF1xUwR1sU3Qp7RimFgRox1pyJyJABz2C5jmqsyf5+xM2n+9toZssbGinLG7sP873dL7ETb2HQIScLcDXs5KTOUFrWTjnsl1gT/kW9pZvZW/5FkYy2wdjUAjI5xfG3uBerPls1vv6FYRhtMYYWXmOjYy9+ec4PJRn5nybsCdQHkSeQnmCqA+Rp1F9i8gD5Qks7dDvhChfJsKkRgXWok+Qiyc9rvZRKbXiacdTQ1dhfuozLFqHUiNssByBFjqOWG6xJGfZtrDATdsEYQgisUlyMT9rXCyU3VyKbt+LRcPkk7WaP2bRJ3QhIVNM43Xi96OQhf2BWGTPQrlYytBq9ZiPCy8EBGhEoBGeQvgR0lNoX5GzbHqNPsJTOL6KbfykvGhbBPE2oV4UKFwvPOr5mzwoX8QtYPEQWdGGXt02KWfEompLJPOQ56fJs2jKfNy+5UhCreNzzU+lHwyl+NvJIICU0KRSNn4Yxe0LgZXcZy3jaczz57MAN+Pg+WH8w8ZiWwHW/INJyumMTb+/YCuTfUokY1PE/YrrNemsS7/nL9wqEXtcz7/eokXjxM24dPseav7bFvHfpvlymBwnBKSyLt1ePJV4xV9HEa/oOX9/FJDNuXQ7HosXuInEogOW23Y9vEXfj5Z85ZnvT3I9uVyabscb/GAYsvC4g6SkiaedZ3MunY4Xe2jr+Wn8Opn1Pr++aQAC0imHXi8YeDGrIH7fj7QmiP9qD9bfDURExk3R8/zYPumL1opIxz9ZRWqh+5Yb/2gCDNqP5vu6YugqHNcm8MJkWyR2Sb8WDW6lwUnZ+H6wZMyvfAeL752wBZG/dK9a/tpZhGULonDp/tXanrcNwgu1lvQDcCyJH63V0jK0RtoStcr78qrt25JonbYQT51fr/3Adh0XKe14iYH1oAHbkYTrtN+I7UbtTT+uzravln5cybavln6sZZ9KW+xOSVppC8ta+BwpxMU/viy0ISlnsou+zwxaWVK3Vnne9lKPu3zniDW0ldrM+tbhvmTBct++ffzJn/wJv/qrv8pf/uVfLtm3Y8cO3vnOd3Lw4MFLbf6yc+DAAer1OtVqlUqlAsSeoFu2bKFQuLgr6jxar3+QPRvZyPVt9F48G9tej20/CpkLehwNGzwxPcP5Xofz/Q5TXpy3wwssXgZUUhl2ZovsSNLOTJwXHRchVgtu8PRf49PR9tXSj9Xsw7BLt3WadusEndZJOq0TtJsnCPzaqsdbdpZ8YR/54j5yxX0USvvYuftGGo1o1X6sVqdUSGP8a9ROfILW+ftI5rgi7Sylna9kaPf3kR66BikFTrqAbi8KoiNsrKG9WEN7Se1/TXyOyCeqnSCafQKne4b2xGNEtRMQ9olmjhDNHFk4uZDI4nbs4f2w/RCeswVZ3IksbkPIC79tXO7n2Aq73HPmb/n09H2DL0p3Dt3IW7a/ln257YM2NtwPpeI1IVse0bkW1rk6ouEhWrEwKZqJIBktbSRk9TdOnXVQRRddTMdiZCJCFnYMUVcRqpCOg6osE6fnEQLEcJrw9DjR+TNEzbOoxlmi5lmixlmmPc0/Fb+PL+deQihisWw0nOYl7S/xku7XyKiQM67k05UMJ/IdAmv+g5PGkWmuGX0+23M3srd0K5tye5HLPiSs+BL8DL8etY5FRq8OUQ/CHqj+fDkRIXssyZXXJQ6Csx4W/xixDKGRKZAOyBS4WYtIRAgbpA3CSfbZIBy9UGfH9eWRDK1ODyyQFgg7ER3tZDsJTi4lVCr5dQfAit8P0uuyj21TVKveOm0dZLW/YBtqRDdCdCLoRIiOQnSiQV1aSey6Dz2F6EeIZGFJscbn+/X/LLwUbYkkYJGIgxU5AlJJXUrgFlJ4UYi2JDgCbQuwxdJysl0YztLs9gfrcGLH629iE29byTGWQNiCkU0FZufa67x/6w9gJwSUKwW663yOuUqB3jpt85UC/eo61hBM7AuVAt4y+9VeFYtt19OPYqWAv4H7UaoUCNbZ9nptL6Xt4UoBte7X18ae+Xrtr5a2TT+evravln5cybZNP56+tq+WflzJtq+WflzIPgh8ZmcnKZRyOM7Cj9y2LQnX+aPHRmyvdp6KhvaUYmg+//nP51Of+hRHjhzhzJkzaK3ZuXMnhw8fRl5lkQt3797Nrbfeyrve9S5+4zd+g1qtxh/+4R/yxje+8ZnumuEqRWnNnN+j6vWY9XrM+klKyvP7OlFw0bbKTpot6dwgHRytMKRSbE8XTTCcqwilQhr1E0yNP5J4S56k3TpBv7vWer0i9ppcJE7mi/tIZ7Ys8ZoUAhwnC7Qu2ge/PU791D/x5Jl/xu9WB/XZkRso73ktpe0vQ9qZC7SwRk+tFHblWpzRa6lUCljVFiqKUK1xorljRHPHCOeOEc0eRfdrqMYZ/MYZZk9+fnEjyOJ2rNJOrPIuZGknVmkXVmknIrXxPl2Mr849xP86+VFqQROA24eu5y3bXsuB/M61D9Ia3fERU+1YeGzFSSa5mK/r+AMvLp/lHpFLUflU7BVZTONuytNLSVQhnQiUiYekvdK3TgiwKgX0IkFAhx6qfZ6oNY5qTaJaE6h2nNdaE+ho6Q8cE/YYnyi+nvuGX4gS8TkOihl+pDjF2PSjnAyO8/kRxZlCfclxxdQo14y8kGuGX8jeoZsZ21S5KiLDR57Gb0DYgbALURfCrojzHkSdRJTsgg67rBHe4YIISyPdeMlNyyUuuyDTYLkaKw2lkTQdr79EmJxPwl7QleMPopkNfXAdqthE1avwx02lY9GxGSFaIaK1UPb8Ku6clwiSCtG/8AfkiDW8SSEO0pOxICPRGYlTSOFLFUdGdwXalWhXwiCP60hJSEuGthSYa3cH4uFazAtjrXV+AbErBVRVru+5CBBy7XMbDAaDwWAwGK48T0mwBBBCcN1113HddddRrVYpl8tXnVg5z/ve9z5+4zd+g1e+8pVIKXn9619/VU1bN1xZtNY0fY9z3SZ136MReDRCj8aict3v05yvD7xkqs7FcaXF1lyBUSfDFjfPlnSOzekcW9JxOb3MFXwjv9wYLj9KBfQ65+i0TtFpn6bbOkWnfYpu+yzLp3PPk3KHyRX2kCvsJVfYQ6G4lx27r1/Ta3JD/Yk8WuP3Ujv5CToz3x7UW26Z8q67GNr9Wtzirqd2klUQ0orFx9JO2POKhf50Z4lqx4nmjmL3JuhOHyOqn4awj2qcRjVOE5z5ypK2ZG4z/cpuIncUkduMzG/Byse5yIwgli9lcAFqfpM/PPVXfGXuOwBsT2/mv13/U+xoDcGcjzgzjej4yLYXe0kuiaTt0Y/08gDrq6KFQOdTWCNZgoyNKqYHAuSgXHDBml++IPYyal7gtauDLqo7i+pW0d0ZZp+cpT11CtWcIGpPoheJ0KsiHWRxG2cLz+PvnRdyf7B5MMXvpiGXl5UmCU59lK/UH6FW6MKiCQLbctdwTeVFXDP8Qrbk9i+Z5n2lUSEE7VhwDAdJLCrHSQcbEyGFpbEyxCnNorJeUmdnYHRrjnqng7jIUJv3KKwu9ih8thNpRDNE1ENkPc59v0FqpheLks0wFivX0CFXEyC1ALIWOifROStOWQvykuxolnYUoNMSnbHQGRkLlOnY05FFY69QKdDegOAryw6Ecl1eggaDwWAwGAyG5y4bFiw/9rGP8ed//ud8/OMfXyFM/s7v/A5f+9rX+Pmf/3l+9Ed/9LJ18nJRqVR43/ve90x3w3CJBCqiF4VJCuhGIX0VIvszTNQatIOAdujTDuO8EyV5uJBffLWcpQhgKJVmJJVhOJVhJJVhxI3zyqJyznYYHS0aEfIqI4o86nOTnB9/lE5rQZjsdcbRevWFfm07S7awm1x+D/ni3kSg3EvKLS+x24jX5Fr068eonfokjdOfIQrm2xHkt9zGzht/CAq3IMTT74ErsyPI7Aip7S8YiOtKaXR3hqh+mqhxmqh+GtU4Q9Q4je7XUZ0pup2p1RsUFjI3isxvQeY2IzNbsFObaec3oVsWIkwjgxTCE4zPneN09TSv9zL8ZPBytkZFCr6N+LvHN3QNOmOjCml0IRWvE5lM054v64KLzqUQlqByEUFFaw1BB+W36Adn8CdOE3Vn0d0qqjOD6lYHiaCz5NjOag06OazCVmRhDFnYipUfwypuZWTXQb4+4/BXp9o8MNtnfoHAG8oB+92vMlv7a77YqQ+WT3SUzb7MDVyz45UcHHkhhdTIhu7RetAalA9hO0mdOA/aYrB9vNsh7G1AhLQ1dhasJNnZRHTM6sG2nYPN2/PUWhefkgvx6zFVkEjvKvRsvBxEGlEPiaZbWKebiNpScVI0wxVi5GrLF2gBOm+hCxa6YKOLFhQssmN52gSoRQIlaRmvubgMIcCpFIjM+53BYDAYDAaD4QqyIcHyPe95D/fccw+2bXPq1Cn27t27tDHbptFo8N/+23/j2LFjvOMd77isnTVcnURaESiFryJ8FRFoRaCiQV2gFemwzkytRT+K8FSIpyK8xeVF25EFzV6fbiJM9qOQbhQS6suzhkPWcig5LiXHpejEwW1Kg5Sm5LiUUy57No9AO0Suwxvo6fBgMqyO1gqvN023c5Zu+yzdzhm67bP0Oufo96ZYy03HsrPk8rvJFnaRy++OU3EX23fsZ3a2c8W+iEdBh8bZz1E7+Qn6tScG9U52M+Xdr6W8+9W4uc1XnReuEAKRqSCtYVKFG2BThPAj8CPoNNH1aVy/Rb86B90eoushvAjhg1QuUqWRKo1QaaSeF2Eb5JedZx+wj63LauPXvrY0Kq1RGVAZgc7KRGCx0TknToU0pe1DNJottIogCtDKA9UBFaJVCF6I7gWxW6AKmUtFdGtVlNdCey2030Z7TbSflP32YA3RxnpulpNFZivIbIVsZQdBahMyH4uTMj+GcIsrAi09Uuvz8W+3+fZ0fAaBZn9unKL+CGH3YU50Yzs3tLmut4/bbnoTm8e+B1teaDL7xVF+7BkZthIPybagFnq0qyIRJkEH6/sDJyyNnY/FRjuXiJE5Pdh28rBpZ55as33xtgRYrngqvwc8+1CxKClnA0Q1iPPZJK/FgqTH2ssXaAt0yUaXbfSQTXosR9eKUAULXbRjkTJnrZhmbQRIg8FgMBgMBsNiarUaP/Mzb+Xtb38Ht9zy/EH9d7/7ML/wCz/D5z//taelH+sWLL/whS9wzz338IIXvIB3vetdbN++fYXNu9/9bn7hF36B//Sf/hMf+tCHePnLX86LXvSiy9phwxVAa5p/eh+ZMz0yK76s6IVIl4toc4EvTRepW629FTZAPEHNWtN+EDhSiMWxm+PgFUvKIrEVWJaIdQcxfx4Fogf0FiqTTEqJWvztbUkALLGirm9LMvMBOsTqdjrpa9+2yERq4drEogtaXAb6jkU6VCv26SXHLJzHc23cIFo4/7L981FCBeCnU7h+OIggu2ThNrGQa+L1vPxsilQ/iKeoipV2g7alIMi5OD0fvYpN3P/kuQhBWExjd7yl7cqF9gCCsI3v1/D9WRrUaTQm6Pen8bwZlA4gid+phQY0aWHhMobjZElltpDOj5HJbiWT20o6t5VUejiOerHouoUPutZHNPpL7wOL+7LwnHQvgH7IIHDxaoi4Z/XTDzD+8N/SPHcvRHEsVFvkyW+5g/LO7yU3eiNgxesqtj2048T9UDpuX8WrFYv5ba1jgUNroqaPnOtAqGK7SEOk4oAxSsXeWVG8T0SawLVxGj0IFCJU8XFhhAjisgijOA8UfaXJ9gLwo9j2IuQZAoYuaqfRaMsnsnrMOT2OZRWzqYCW02dnMMe1/RmwuijZJZJdlNVFy2UBrPpJmltavS5RcRHd9RpaKexMGZ0eGQiSMltBLCrL3CjCiSekr2f5h4luwP89Wuf+ag8AiWKTcz9l/go3mCYkFimvn9vGjd2D7L75DcjrD1FZh0e3imJvyKgN0+cCapMQtBIhshXvU/5qAzdk+YCWbixGOnliUTIfC5FOAUa352gFHYRz4R9vhADbFRuKkPicxFPIaR857eO3WqTOdZDVADEXIFZ3/AZAOwJZSREWJKpso8tOkscCpc5bA4/I+fUdL7R8gcFgMBgMBoPh6UNrTT+6wIe9RdhCE0ZP3WHLlXKFo8TFePjhB/mt3/p/GR8/N6jTWvOJT/w9733v7+H7Fw4qfDlZt2D54Q9/mG3btnHPPffgOGtPURwbG+Oee+7hrrvu4s///M+NYPksQGtNu9ZmTK+28Je4/OtIXdEvTytlz+WINcqrsZHVWPUG7Ddqu/7V/2JftPW+sKMN2M7br3eCcshg5upFCbhw0BOAhZAuNlBhK5V1tr6cDnB0zb0erGv9Q4i1so1Evz3IrcCtSysfg/gngKW/UvU30A+fxffn4mzk2aymxWohwLXQKQtSFjplQ8rCybt4FmjXhrSDTtvojI1OO5C24+0kCdfm/2fvvuP0Kuv8/7+uc85dZ+7pk95DEhJ6L1KFUEVQUWwglq+LuKKuddXf4uq66loWdbHiyuqyNhQQJBKkCUhvgUBI78kk08vdz7l+f9z3tGSSzCQzc88k7+fjcXvOfc51X+dz3zMZmfdc57pSZSlufPYWXmpfCcDhsancMOGNTLceNtNGkGnHSbdhMm1E3BzpZBKbz2KDHPjZ4ujJXGHrZyHIYf0cDj4BDsYJFZZldkKF1c0dD9wQxriFbfFYtLySrI1iwuWYSAITTmAiCZzi1oQTmHA5TigybCNfO3MBv1vfxj2b2vGtweBT4zzIBPcewqaZcBDmyJ0zObZxOvO7psOpJ5M96WgIeYUfy4El3wXZ9uKt2h3F27SLIyXzneAn+37lsuzpp06/MDIBlRPCZJ1M72jJ8sLCNAMxBuJ1DsmxuMhMqeUCnJ05nIYspiGLs6Ow77T2zpO7623b1gVbGyIoPmxtiKCusKXSHVRQLSIiIiJji7WWTz/7Gq+27ftOo+G0sKKcbx53+KBDyyVL7uGWW37M9dffwI03fqHn+Ne//hU2bFjPBz/4Yf7rv24aoWp3N+is4uWXX+a9733vXsPKbolEgiuuuII777zzQGqTUWIch64PnsD9Xe1kUzlcHFzHwTMOnjF4joNb3HeNQ8hxqKkqI9WewSs+Dxu32G6A+a6A6poyWpr3cJttn4PGQHV1sS3sYbhmn/Z763uXA4buvjsLIcyA2WZh9JoxUFUVp7UlWezGDpCF2p59A1RWxmlr3aVu278NtrBTWRGnrTXZ7/yu7bGFpTYqKmK0t6UK8+j1qcX0rau4byyUl0fp7EgVuuoeide3jj79lMcjdHYWJ33radu3jkKf3cfjsTDJZKaQivZ7Te9+d13RSIh0Kttz3Po+fj5FkE8T5NP4+QxBPkPgZ7B+lsDPFj8ng7EOYDDWFEbIFvcdJ4xrInheBEMIxxQexnjFOnetqzDgKPBtT517ate9NcUwqOfrYfcdbI+I7lGojuk34hSnONKzzzk35OJjsW5xzjnXgOtgncIWt/i64n60PEo672M9B+s5EHKhZ7+49VwIOVTVJ2hJZrB9gklcs9tQOmOgfJAr9gY24K6Gh7l1491kgiwRJ8S109/MmyedjWt2D9WGslDVUBe1Gu1FsHxruXtjA79Z10XKDwGGhHmRKd7/Uem1cnhuPse+fiTzm2diqSI553B2HL+AXC5M/sHeOSRXdiWx/iCmq3AtXgLiNS424heDyeKt24lCSOn0Sa8Ln0eYxsaMQrHBshbT5uNszeBuy5BpaSK6MVkYMbmHzzBIuNiJYSIzykiWW/xiOGkrvQHnjQRNPSIiIiIiI+vkk09l8eKL8DyvX2D5oQ9dx4QJE3n++WdHtZ5BB5ZdXV1MnDhx0B3Pnj2bjo5DafKp8e2Zzd9lVctTFOMiDN1Dh01h7jqcwvHivus62KDPuZ5td7vC1jEuxhhCGzwCn542jnF7Xte975jCI9YUJZsNcHBxzJ4eTs9+wpaRTuZxjIdr3MLW8fo/Lx6rMRV0hnO4JoTrhPCKW9eE8Ipb1wkRckM49VUEUXfQoYdblyBoDA0qUCm0HVz44g5hbjFjwKtLkB9k34Nt290+VJcgN0B7P58ik24ik2kkm24km2nE0EpL8ybSqR1kkg3kcm37HEbpuFFi8cnE4lOJlU0hFp9CND6FWHwq0fhEHCeEMVBTDJjyoxle9QuXC8FnbV2CpuKCNKmW12hbv5S2zQ8T5HtvMo7XHk31rMXMPOIi2jqKyz7tctv/rreej3RAN9hbRY0Bpy6BHcYwL+mn+dbqX/JEyzIAjqmYz8fnvIsp0frhucAYlckn+fOGZ7lrQwKyE5iQK6c238YM+wpz3DomJr9OoilGLigjZ8p5raw4rrqh+BiIsT0jIEOJ3lu1u/dDCXCihZkP6upiGpk3HKzFNOdxtmZwtmYLIeXWDCbZe8uOT+9YVhtzCCaG+zxCBBPCEHcHteq8iIiIiIx/xhi+feIiurL5fTcGPM8hP4jpuPZlqLeE19YOfBfjhAmDzwKH06ADy/r6enbu3Dnojpubm6mtHf4VS2X4WWtpSW8nH+QG/6IhNB2vHOPimTCe0/sIOZE+zyOEurdumMSmBH7OJeRECbtRwk6UkBsj7MaK+9HCvhvFS9WTyVs8J4ozwIiyscBaSz7XQTbbQi7TSi7bQsuOLpqaNpNJNZLNNJFJN5JNN5HPD25ou+vFicYmEolNJNr9iE9k0qTZZHJVhMI1Q55jY9T0m4+0EDBm0jvZueoOWtb/hWznpp6mofJJVM28kKqZFxIun1IIexMJyHSM8JQIY9v2dCNffv0nrE9tI2Q8Pn3kezi77ERKNIZ1WFkf8knwk4VtPgn5roC1netpaGgn21XLzNyZfNHvO8HDZODwnmdd0Oeu7eKK2X1u1fbKesPIiTPKaM92HQwf3dhVDCfza1oIvd6GsyWDsy2LSe/+H4/WgWBCGDs1TGxuBR3lQeF5uauhkSIiIiKCMYaoO7jJ3jzXIb/HxTwOHYMOLI888kgeeOABrr/++kG1X7p0KbNmzdrfumQUGWO47vifECpL09zcSWAt1losQeE2ZHr3bXG13sqqKC0tXQQ2KJy1fr/XFLZBcRkUn0QiSmt7F0HgY21AQIC1fvG1hf1CXwHxMo+OzhR+4BPY/g9LsNuxcMTQlepun8cvPgLr4wf5nmOB9TFuQCabwbc58kEOP8j17tv+KWxgfbI2RTZIjdxnjyHsxom4cSJeWWHrlhH1yorbONU7qrG5CFE3QSxUQcxLEPcqiHkVREPluGZw/4wDP0su10E+104u204+30F7c4bmpu1kMy3ksq27ba0d3KTAUBgdGYnWEYnUEYnVUlU9BUtNTzgZiU3E88p3CyRH+5bcA+XnOmnf8ihtmx6gq+E5uhNI40apnHY2VTMvIl5/DGaMBtGl8lLbSr626ue057uoDlXw5QUf5oxZR43Zr7u1YHPgp8FPQT4FQRoyTo62naZwa3Z3QNkFQWag/6BwgHpq2GX0qFNcsMZJE2nZRjjfTogU0WOnkV0wGS/h4JWB2cN/zxgDkSoHo3kjh1c6wNmSwd2YxtmUwd2UxiQDsvSfu9e6EEyKEEwJE0wpbieGIeT0jGAezAh6ERERERHZs0EHlm95y1v4yEc+wq233sq1116717a33norr776Kt/4xjcOtD4ZJZ2veeR3VJPJJHYfATbA7+HJiAfZPE6xrbuX9gaIRENUpXM9Xe/WZZ8D0WiIdCbHnhv3eZkp9J3p03e/l+yywk4sGiKVzg3QEHpWmS4Go+GISyqdxu8JVH0C+oSl5Pvs+zieJZPL4AfdAWmuJzz1e/YLwagf5AqhbnEySkt3MFyoAyxpY0kDrXRgaQezo0+7QnBsjcU1Lp5xcU1hDtGQcXFsgGsDnMDHCbIYP4Mb5HCtxem5ZlC8VkDvxI5hLHUYagibwjnXjeGG43heGfHyanDihMKVhCMVhCJVhCKVhCNVuF4U4xRXZjfFuUVbu3pW1DaFNVIozjQATvFOaAeCvMUGxQVexuAfkgI/Q8e2J2jb9CCd254oLPRSFK8/hqqZF1Ex9Wzc0GCXyTm03LP9UX604ff4NmBe2QxuXPBh6iNVo3b9IA9BprA4+65bm4V2MnQ2G/xUb0Dpp8D6A30zZtnTDyVrAtKhFrrcHXR4lp3uRNo8lzYvT2Vljkvn1zBtQgjXZIk+8DjhZSsA8CfUkrn8fGoWzhyzAe7BxgYW05DtH07u2H3OSeuCOyNOZqJHMLkYUE4IgzcGf1CJiIiIiBxEBh1YnnvuuVxwwQV885vf5KWXXuK9730vxxxzDJ5X6CKXy/HCCy9w2223sXTpUk466SQuvfTSEStcho+1sPNxg5/2Gez9hR0Mvm1BftDt24fQdqh9t+21bffxvvHr7otMmWKLgQY/DWWl5vEsx75nBVhHisGuhb6KZG9bYwuhZWHdnZ6A0/R5vj6UJAgKC9H0HO9zvu+xhmiaXN70PO/Xvvi8+3rGgc6yLOmMweKTS20l3baKTPsqbJAGUwVcQDheRbxuIRNmHU8miIOF9NZd+uyz77iQdgJyHfSGtc4ANRxk8oHPjzfczj0NjwJwTu0JfHLue4g4A69Rbov5eZAv3GJtu7c+tCd9unYWFgcPchBkCyMgg5zpeR7kCo+tNkWmy+CnC8HkwMFjv0rZ088F41rcGIVHFOJVHr6Xx41ZbDTD5tyLvJZ8kNXpv5N1O8hRwVb/A7QGxwMdzEiEuXZuJcfXVBfm7N28ndjdD+C0tmOB7KnHkj3zZExocLeHyH7KBTibM7jr07gb0qQ2ZYgNcGt3UO0RTI/gT48STI9gp0Som1RBp4JkEREREZFRNejAEuDrX/86juOwZMkS/vKXv+C6LlVVVfi+T3t7O0FQuB148eLF/Pu//3tPmCljmzEw7c0Wpz1CV1e6/wDLgX5BM1AWj9LVld793B5+oSsri/b2PVAbWxhlaLDE4uFC26A4AtDantvLKd6uXlymGmst0YhLKp3FBoXb0Om+ld322RKAtYTCDpl0ttBf8cEA+1iL40I+l+ttGxRvY+/7mmCX1/X9kHpWV+l+sMvWFEfzmD20M2BN8UjveWO84iOENR4YB2tcLA7WOBjHkA8sgS2sxhzYwm33vvV7aiqsvl1YGImelbidPscdHIqLFZkQLiEc4xFywhjr4uBicHtWKO9dkbz3FlWDKa7O3ff8IFI5a3qCqz19u+QLYzH33ReQHGK43kKu2N4DZgAzcDivf4nt0LUd1r0y6G4HEeDafgHmGq8Li+kXsA4YtjqwLZwiXwxlu0eu9oS9AzxviWbIZEz/Ea27bA2Ftu3RDKmU6f0aQu/Xmv7HmiMZ0imD9SHn51nVsYnpuWP4R3s8U8L11G2uZvsLQOGfDBtIks+Y3oDSZy/fI+l9fH59Bez+Nbc4EXAj7LYtrwmTtRmcaCGY9PoElCbUZ/pSA9U1Hk+tfpQXG+7jtabHyAfZwknX4EXfxevJxaQDD9fAO2ZXct0JU2hr6cLmfcKPPUf4iecx1hJUlJO+7Dz8GVMG+Z5kSFI+7oY0TjGgdLZkMLvMcGFDhmBabzgZTI9gE/3/u+Vg/EOCiIiIiMi+PPbY7iuCH3/8iQMeHylDShTLysq46aabeOyxx7jzzjt5+eWX2bFjB67rMmvWLE466STe9KY3ceKJJ45UvTJCtrf/ivaWZ8ll873BIIWwDyiGcd1hocXtMOTz+WK7oP+2p23v1jTanvkrdwsJOfDVrwZtgIy1n76ZIQw0wHKQHFw3glN8uE4Exw3juFFcN0IsVoYfhHDcWOGWa6+w9bxY4Vj381CM2tpaOrsMrluG68VxnD3/s93bfJDWWjJ+kq5cC53ZFrryLdhQJ9uaN9OeaaQts5P27E7aMzvJBfv6oMAzYaqik6iKTqImOoWq6GRqolOojk6iOjaZ6ZOm7FaH7X4S9IaYBqipLqepsZMg6H+uO7jsuzVAZUWc1pZkb/vubHuXLRbKy6J0tKcJ/D7nin0VvhUtua6dZNo3k23fip9LAy5YD8eJEYpPJRSfgutVFsLUoLcPz3PJZvxi0FYM3frUYPte0xqsb3v2B/jq9dZGYcTgYIPW1IAB3Z4NZRRz65BGPPdtG2IGc/qd3f27au/Bs3EtxgPjQSjiYJ0AJwQmDE6ozyMMTsj27FfVxujKpjDh3mDSCQ8cPhX+zYRpbMzscQSdtZatna+zbOdfWf7Ug7RnmnrO1cVmcFjtZTzVcjrPtRR+ls1NhLhhYS2zK8KEXAfT1ELsrgdwtxcWrssdOZ/04jMgGtnje5ehMW158mtbCL3cirs+hdOw+xjwIOESzIoSzIpScWwtzeEc1lEiKSIiIiIyFu3XEMgzzjiDM844Y7hrkRKx1rJ53R/JZppLXcoeGeNijAvGKe73bl03hKX7uVfYOi5O937xuTEu0WiUXL4wStFxvOI2hHFCGOMW9wvnEolyUimLcUI4TgjHCeM44cJzN9znWAjXDVNXX0NrWw7HiRbr2MMtpkNYZMYYqK5N4NsDvx3RGEPUKyzoUxub1ltH5e6hYtrvpD1TCC/bs43F7Q46/Z3s6NhEW3oHeZulMbWRxtTGAa9XFq6kNjqd2th06mIzqItNpy4+neroVDw31Dugz4AXM7gxeuZE3ddnkqhzyUT3veBI4T2GcBrT/doGQY6uHc/TseVR2hsex8+0FE7EwK2Ik5hyBhXTz6N8wgmYfgGx3aXv2KC/jnV15T1trbU9QavtE7p2P4yFqsoympu6etvYXdr2DjSmvDxGR1uq93jf8LZ7ZGSfc2XxCF1dmd4RksX6bd8gtXgsHg+TSmV7p5Q1dvfRmMWgv6wswqvN67m/8UnSZEiEYrx58tnUx6p2vwXegeraOO2dSXDB8cC4hXDSuMVHn/7r6uKD/qxr6zzsMCxI05zawks77uflnQ/0+z6Phyo5qv48jqm/kBWd0/jvVS105QM8A1fNruStMyvwHAPWkn/8BeJ3PYTJ57HRCOmLziK/8LADK0wwrXmc9SnctWncdSmc5vxui+MEdSH8mYWA0p8VxVZ7YArz7Dp1MWjM7/GuABERERERKS3dsy0YYzjhjB9hgo10dKQL4V+fyQNNz9YptneorIzT3p6h+/5UU7zX1JjCLcWFXwqdntfX1CRobU0XbnE1brF/pxg8OsXXFm5nrqurpLk5VTzm7nW15aGGfyPRtrt9LJ6gKzn+5zkzxhDzEsS8BBPL5vQ53vuZ5IM87ZmdtKS3FR9b++xvoyvXQle2ja5sGxvb+9837eBSHZvcE2TWx6czl8OJ5CYS8ypG7H35+SSd25+mY8ujdGx7kiDf1VtTqJzE5NOomHoms454Iy1t+RH9OpriRKh7WwU6XueQdAYbynqDXjF67yMK7RDa7v7aP7X8hR+2/AHq4ZiK+Xxi3geoCJXvsY6KOpfsGFvpujPbwvLGh3hpx/1s7ni157jnhDm89gzOPOwyJrpH05yBH65o5rmmwh96DkuEuWFRDTPLC/NzmmSK6D0Pkl+zEQPkZ00j/aZzsYmBPw/ZO9OaLwST63oDyr6sAXdGjMy0EP7MGP7MKJRrXlARERERkfFKgaUAEItPoq5u3pDCPDc8+DCvoipBNj+4vkOhMlw3GFMhhvTnGo/q6GSqo5MHPJ8NkgSRVlZvXcHO5EaaUptoTG2iMbWRrJ+iKbWZptRmVvJE4QWrCptEuI4J8dlMLJvDxLLZTIjPpj4+i7Ab3a8685lWtq14iK0r7qez4Zl+q3t70ZrCSMqpZ1FWfyzG8TAG3FAM6Niv6x2qfBtw09rbuH/nUwBcNvEs/mHm2/Cc8REYZfIpXmq4n5d23M+almcJKEx2aHCYU3U8R09YzMLaM4mFyqitLefXy7bz3ytbSPoWz8C751RxxYwEbvH2Yqephdjv7sVpbQfPJXPOqWRPPEoTIg6Bac2TX9VMeFkrzkABpQPBlAj+7CjB7BjBrCh10yq1OI6IiIiIyEFCgaWIDLuIG6euaiKx/JTdbjfvyDYWwsvkRhpTm2hKbaQps4nm5DY6so10ZBtZ0/pMz2sMhurolGKAOYeJ5bNZGDkGx1YVFv7pwwZ5Us0r6Gx4ms7tT5NqeZ2+owbDZVNITD2TiqlnEatZuNfRuzI4vg34zzW38dfGp3CNw0dnv4NLJoz9KUNyfobVLU+zvPFhVjQ/RtbvnWFzSvkCjp6wmKPq30giXNtzfGc6z789soGntxdG586vCHPDolqml/XeiOyu20zsjvswmSxBVYLoh66kMxzVrcf7YNryOOt2v8W7+z9Sdg0o/ZlRiPb++1UWLCIiIiJycFFgKSKjxhhDRaSeikg9c6qOLx4rjNjdvH07O7rW09C1lh3JdTR0raWhay3JfBvN6S00p7fwWtNjhY5eg5ATZVLZXCZEp1Gdg/KORkI7X8fJdfW7ZnndAuITTiMx9SwiFbP3OLeoDF33yMq/Nj6Fg8PXjv8Ix4UX5WGDwAAAfz1JREFUjtkRblk/xarmp1je+Agrm58gG6R6ztVEp3L0hPM5uv586uIzdnvtkzuTfP/VJrrylrBjePecSt48I4Hb5/sp9MJyIvc9irGW/LRJpN92EfEp9dCoEbu7Mu35wu3da1O469I4Tf0XybEOuDPiZGaE8LsDyoj+wCAiIiIicqhQYCkiY0LUK2N6xRFMrzii3/HObMsuIeYadiTXkAvSbOpYzqaO5T1tTTVU+A71oXqmVCxi5qRzWDjvLFLtzpgN0carwAZ8b+3/cf/OQlj5+XnXsnjKyTSOsXAuk0+ysvkJljc+wqqWJ8kFmZ5zlZEJHFF3Nm+YdykJfxYDrVieCyy/WNXCnzd3ArCoJsY/LqhiarzP8i5BQOTBJwg/s6zwmiPmk77kHExofNwSPxpMex53Q5rs1jair7bjNO4SUJriCMo5usVbREREREQUWIrIGBd340zKuyTakkxu3E6yaQO+n6PThZYQtHrQHiuj2c2TJkObF9BmG1jd1gBtD/Gr16EqMokp5QuYXD6PKYkFTCmbT1m4qtRvbdwKbMBNa3/N0p1P4mD43GHv4+y640tdVo9UroOXGu5neePDrGp5mnyQ7TlXHZ3MorqzOaLuHKaWH47jGOpqBl5ga1syx7deaWRNRyFce8vMBJ88ZTqtzZ29bTNZYn/6K97qDYWnZ51M9vTjD/l7lHtW8V6X7jeCMg84FAPKSWGCOTH8OdHCCMpYb8B7iH98IiIiIiKHPAWWIjKmBPkUyabldO18iWTjS6SaX+u3WA5AJFZHzYQTWTTpZMomnIgXqeyZH3Nb5yq2da1me3Hbkt5Ka2Y7rZntvNr0SE8flZEJTC6fz5Ty+T1hZkWkdtdyZBeBDfj+ut+wdOcTOBg+c9j7OLvuhJLWZK2lMbWBlc1PsbrlKTa0LyPf53umJjqVI+rP4Yi6c5hcNm9Q0wL8bXsXP1zRTMq3JEIOn1hUy0n1MTyn97WmvZPY7+/F3dGE9VzSb3oj+YWHjch7HOtMSw53bbowD+W6NE7L7qt420lhwosq6Jrskp/VP6AUERERERHpS4GliJRUPttO04aX2L7mSbp2vlRYKMf6/dp40RridcdQVl94TJ1zJE1NXf1GxPWdH3NB7enFYxCrsCzf8AJbOleyrfN1tnauoim1ibbMDtoyO1jRPS8mhVXKZ9UspDY8h8nl85lcNo/KyATNe1kU2IAfrPsNf9nx92JYeQ3n1p1Ykloy+SRr255nVfNTrG55mtbM9n7n62IzOKLuHI6oP4eJ8TmD/hpm/ICfrWzh/q2FuVAXVUX41BG11EX7/9+ls7WB2O1/welKEpTFSL3tYoKpE4fnzY11gcXszOFuSONuTJPasIlY88C3eAezo/izCg8TdymvS9De2KFFiERERERExqiWlhauu+79fO5zX+L44wu/7z388APceuvP2bp1CxUVFVxyyWVce+2HcJyRm2degaWIjJognybVupJU8+ukWl4j3byCbNfW3dqFYhOIF8PJeN2xhMun9gROxjCk1b3LwhXMqT6e2VW9tyyn813FkZgr2dqxkm2dK2lMbaQj28jL2x8FHu1pG/cqmVw+rzgas7Ctjk7BPcRWGC+Elb9lSTGs/PRh13Bu3Umjdn1rLQ1da1nZ/BSrmp9iY/vL+LZ3FJ9nwsysPIb5Nadw8txz8TJ1Q577cGNn4RbwjV05DPD2WRW8c3YlrtM/7HRfW0P07gcweR+/vobU2y/BViaG4V2OUdkAZ0sGd0MaZ0Mad2MGkw56TluKq3hPi+DPihVCyhn9V/EWEREREZGxb9myF/na177Mli2be46tWPEaX/3qv/CVr3yD0057Axs3buAzn/k4sVicd73rvSNWiwJLERkRgZ8j1bqKZNMKUi0rSDWvINO+DmywW9tY5Qwi1UdSVnc08fpjCJdNHtHaol4Zs6uOZXbVsT3HMn6Shq7VtLOJVduXsbVzFTu71pPMt7Gm9VnWtD7b0zbixplcPo/ZdYuodKczMT6H+vhswm50ROsulcAG3LzudyzZ8TgGw6fmXs0bRzisDGxAY3IDG9tfYVPHK6xvf4GWVEO/NjXRqcyrPpnDak5hduWxhN1YYdX5RILGzOAX/7HW8tetnfx4RQvZwFIddvjkEXUcUxPdtSH5+58gtqQQaOfnziB1+WKIhA/4/Y4ltj2H+0oXTjGgdLZmMLv8s7UhQzA9QjAzSvkxNbRUBdiQAkoRERERkYFYa0n7u/8uPBAPyA+y7d5EHDOkuwWXLLmHW275MddffwM33viFnuPbt2/liivexhvecCYAs2bN5qyzzuGll55XYCkiY1s+00q6bS2ZtrWFbfs6Xm1bS+BndmvrRWuJ1RxOrHohsZrDidcsYOKUKQMuejKaIm6cmZVHU1f3Bo6qvBRrIRdk2NG1lm2dq9jauYptnStp6FpLxk+yvu0l1re91PN6g6EmNo1JZXOYWDa38IjPoSo6aVyPxrTWcvO63/PnHY8Vw8r3cl79ycN+nUw+yeaO19jU8Qob219hc8erpPOd/dp4TphZlccyv/oU5tWcSm1s2gFfN5kPuPnJLdy3oQ2AY2uifHJRLVWRXeZXDAIif3mE/EsrAMieeBSZ806HEbwFYlRkApytmcIIys0ZnM0ZUi15Irs0CxIuwczC4jjBzCjBpDC4BmPArUuAbvMWERERERmQtZbPPN3Aa627/348khZWRvj6CYOf4uzkk09l8eKL8DyvX2B5zjnncc455/U8z2TS/P3vj3HBBRcPe819KbAUkUHzcymSzStIt/YGk5m2teQzLQO2d0JlxKoPLzxqCo9QrL5fm7E8PWTIiTA1sZCpiYU9x/wgz87UBrZ1rqQ92MS6xhVs71xNMt9GU2oTTalNLG/sXdwn7MaYWDaHmTULKHcmURubQV1sOlWRSbjO2P4RbK3lm6/8insaHsVg+Ke57+H8+lMOuN/ABrSlt7F241qWb3mWje2v0NC1Fkv/vyKGnCjTEguZUXEkR00/mRqzAM/ZNUrbfxs7c3zj5Z1sSeZxDLxnTiVvnVmBs+s3ZRAQ/fNDhF5ZCcaQvuAMcscfOWx1jBrf4mzL4mzuDijTmB05zK5Bo4FgYhh/RhR/ZmEUpa3yxvY/VhERERGRMWw8/Jd0bW3dPtskk1186UufIxKJctVV7x7Resb2b8siMuqs9cl1NZDt3EymczPZzi1kOzeT7dhEtmsbAw+jMoTLJhOpnEO0+Jg8+2i6clXAOB+BtgvX8ZhUNpfJ5XOpq0vQ2NhBEFg6c800dK0tPtawvWsNO5MbyPopNrUvZ1P78n79OMalOjqF2tg06mLTqYtNp7b4GAurlVtr+fGG27lr+yMYDJ+c8x4W1586pD7yQZam1GZ2JjewM7mBxtRGdiY30JTaRC7Y/a+LVZFJTK84ghkVRzK94kgmls3BNV7hNu/iZz1co3Cf3JnkP5c3kfYtE2Ie/3REDQsrB7ilPwiI3vMQoeUrscYQvvoyOqdNHfujCXMBTkO2EFBuz5LesZ3YxhQmv3vhQaVLMDVCMC1KMC1C9dG1NCWTJR3xLCIiIiJysDDG8B8nT6Ir6++7MeC5TkluCd+XjRvX88Uvfpaamlq+//0fE4+XDVvfA1FgKXIICoIcfrqRlvRrNG9ZSaZjS09Amevahg1ye3ytG6kmWjmbSMWcwrZyDtGKWTherKeNMRCvSpAs8W3eo8UYQyJcSyJcy2HVvXM7+kGeptRmGpJr6LBb2NS0hqbU5p7ArntE5kqe6Ndf2I1RG59M3K2hIlJHIlxPRbiWRKSeinAdFZF6ykJVOMbdtZRhc/u2B3rDyrnv5oIBwsrA+iRzbXTlWujKtbCqq4O1Da/3BJQt6W27jZrs5poQ06sWMCW+kOmJI5lecQQVkfoB2w6nwFp+s66N365rB+Co6gjfOHsWfmdq9+/VICB694OEXl2FNYb0FYuJHXt44fbnscJaTLtfDCYzONsLAaVp7D9yMqDwV10bc/CnRgimFR9TI9iK3v8UMAZM3IXkqL8TEREREZGDljGGqDu4wTye55Dfd7NR9cQTj/HlL3+Ryy57C9dd94943sjHiQosRQ4y1lr8bAfZrgZyyR1kkw3keh47yCUbyKeb2NsQMeOECJdPLT6mES6fRiQxjSmzjqQ9GTokQsjh4DoeE8pmMbF8Vr8RgoEN6Mg29oSXjclNNBbDy9b0drJ+im0da4G1e+zbwaU8XENFpJ7qsjqMHybsxom4ccJujLAb69nvt/Wi5NrjNHW241ufwPr4QZ6guB/g81L7ev57298AuGLSkUTTq7l3zTN05VrozLbQlWulK9dCMteG3cdQw4hbRn18JvXxGdTFZhb3Z1Idm8TE+upRnbs0mQ/4z+VNPN2YAuCy6QneP6+K6qhHY+cujYOA6N0PEHp1NdZxSF9+Pv7CuaNT6ECshS4fZ2cOpzFHtqOdyNrOQjiZGjgUtmUOwaQIweQwZQsqaauyBNW6tVtERERERAbvlVde5gtf+Ayf+tTnedObLh+16yqwFBknrLUEuU7y6WZy6Sby6Wby6SbyqSbymWbyqWbymWZeS+3Ez3Xtsz/jhIgmpuDFJxMqKwSShXByKqH4BMwuo/eMgXA8AckxNLpsnHKMQ2VkApWRCcypOr7fuXyQozWzDaKdbN6xkbbMTjqyjXRkG2nPNNKebaQz20yAT3t2J+3ZnWwexi9JkiivenOwxmWC38TmTf/Hlr20NxhiXgXl4RpqyidQ6U3pCSbrYjNIhGsHvA1htDOzzV05vr5sJ5uTeUIOXH94DW+cXD5wHUFA9E8PEHqtGFZesZj8gjmjM+9M3mKacsVgMospBpTOzhwm3RtM5oHuf6HWAVsfKoSTk8IEk8MEk8LYchdMYWEcry6B1cI4IiIiIiIyRL/61X+Tz+f53ve+zfe+9+2e40cffRzf+c73R+y6CixFSsQGefKZDvLZNvxMO362DT/bRj7Thp9pw88Wjm3wO0h37iSfbt7rrdq7csOVhMomEopNJBSfQCg+iXB8AqH4RELxiXjRKurrK0q+Orf05zmhwmjEugT1zqIBvza+zdOVbaE920hHdicmkqa5rYVMPkXWT5Hxk2T9FFk/SaZnWziWC9K4jgs4OLg4pvBwjUcWj5fzMQIM9Y7lzGgtlWVzCNsKykLVlIWrKA9VF/erKQ9VEwtVjNg8k8Pl2cYU33mlkaRvqY24/PPRdcyr2MPiPb5fCCtXrCmElW+5gPz82cNXjLWQDHBacpiWPE5rHtOcx2nNkWrdTGxndvdFcLpfasBWe9i6EJEZZXRVmkJAOSEMnkZNioiIiIjI8HjssWd79r/5zf8sSQ0KLEX2g7U+QT5NkE8R5FNYP0lLOqC9qZF8tpMg14mf7cTPdRX2c50EuS78XOHYilwH+ez+DYtzQuWEorV40Rq8nm0NXqyWULSGCVNm0Zkpw7gDLCDSh+4KHb9c41ERqaciUo8xC4cUFO4pWMwGOT776vdIda5nanQCNx35aSpC8TEbQg6GtZbfr2/n/9a2YYFFlRE+d1QdVZE9zP3p+0Tv+iuh19diHYfUWy/EnzdraBfNW0ynj2nP43TkyeXShDZ1FsLJlhymNY/JDvxhWorzTEYMQV24MGqyLkRQHyKoD2NrPAg5GAOJugTt4/TrIiIiIiIisi8KLKUgHeCv7cJpS+/zF2BjwG939tzWDNC+08VpzfS231NYZiDocjFtmT2OMur3WgNB2sO0ZMCCtXlskMMG3dtc7zGbo3WTR1dbG9bPEgRZbJAl8LPYIIPN9x6zQZZG1yeT6sTPp7F+msAvBpR+GhtkKMQLhSJ7/rentsK5vmcLb6j3mEsMTCGAdMMJ3HAFTiSBF6nAjVTghAv71fWTSOViuNFqvGgNjrfnINIYiFcfOovdyPCw1vKfa25jRed6yt0Y/7rgH0h48VKXdUBS+YCbljfxxM7CfJUXTy3ng/OrCTl7+OHj+0TvvJ/QynVY1yH1lj5hpbWQsZiUj9/ehbupC9rzmI5CMGk6fEyHj9OexyT7zyeZA0IDXC5IuIXRktUhgioPW+NRMbuC1nCeoHgrt4iIiIiIyKFKgaWAtUS/v4lMq8/ex+T1ysCg2w61fRqI7bPVUNuH6I4N6hn5lYiHWxlQeKdbgcKtoT3hqKE33DCQdAwxbHG53/5tel5XfKQ8h2hge/twes/Z7tf3OZcOuUT8oE87g3X6tymcM2RizYRz+d5rOqbnXKFd4bXGMWTLOwmlsljH9BzvbtPzuj7bfJWP25kutjNYdw9tXUOQT2Pacj3HrNt7rl/7Q9hvtt7HQ03P4uDwxfkfYlpsYqlLOiCbO7J85pkGNnbl8Az8w4IaLphaDr6FpI9JB5hMAJnCNkeG6AMv4OxwCcwR+JOnEXokTHjJJugKMCkf4xf6zgB7uJm8h3XBJjxswiU8MUa6jEIoWe0RVHvYysJIyb6MAVfzTIqIiIiIiAAKLIXC78ZNscdJJAe/Au4AS2j073Afrfd23Az0zO67jwFf15O4FRYHwZhCX6b4vM/5fu2KW9vnnLEUX7/LdYpDGY3tc6rvoMoRYHoHd/a/YHHf7Has+Lpdnlt8nN1a7VlA70If++Iz+B8weQYehbYnWfYdGnUbTKBtDcUAE5KeQ8wUQqeeUNOlEKa6hX0cA54hHd1J2Pd3b9MdiLrFUNYz5BJdeOlsMZDt04/bJ3Dtfq1n8HeC05EqhrjsOZj1wEbykPIpDNll94B4Lx5reoH/2XQPAB+d/XaOq1wwyE/2wNnAQr748C0mbwvfZH6f577FWPB3gtvYhc0Wj2cDyFlMzha3heetnTnWNmf4xxxU+DDNdQg/1YTJ7Cy0HUAOcJgATADA3QCF75xd6g0ZnIRHvszBJtxCKFnRuw2KISVxp2exm0Rdgg6NeBYRERERERkSBZaCMYbW07exdcefCKzBmMLwNmOc4ig9t7DKr3EwOGAcQuEQ+TzFNk5hRenureP2O26MQzQWJZu1hb4cr9hnn32n+7lLoqKcrqRfPB7COF5ha7zefadwznE9amqraW3PYkwY44aK50PF99H3fQ5+UZChLiCyz/a2N800BmprEjR1t+1+Qffr+oSRBkttTTlNTZ3YoPe86e6v32sKQWV1dRktzV2F9n3b7fI6A1RWxGhrTQ7YFmsxQZ99C4lEjI7WZKHkoPc4ARDYftcpi0fo6kgXzxXPd18n6Nu/JRYOkUrmetsExXN9nvc9HnIdcpl8b7gVgOlu49ue9qYYdtliAEYwcIhsLIXQDCDr94ux9yZg8D9Ec0B4kG1haKOSU8CebuDefYQrJF2HmLX4BJwchLiTDxNxw8S9GJgNvaNzDaRch6gf7KH3XepwHKL5oPfz7/7+7vv91ed7IGX3XPeuBjOyEaC++Ojl79bGhgw24kDEYFIdmEwXOHny8yYTTKrCljnYuIuNOxB3e/ZNxBnXc3qKiIiIiIiMFwosBYCpJ3yqdGHeAfYdr0qQzI/xAGGX27ONVxihVwgc9xKNGTBlHqTcfgMm9/hWDTh1EazJDvh59D1UuAW1jKAxGPRn7dUl8BudQX0dQ3UJ8oP8mlcOYQGR7lFrnfv7/bSHYLN7JF91RZyWxs5CztXdxi8Gon5hvxCcWhLxKB1tqULYWezTFNt3v9YU92PhEOmuLLa7Td+RhMHu13KNg5/1+4e23XX0PN9zCNvzGXQH0EHfRoVQ1gOq+0WGA4R7MOhRuENpO+Dru0e6Fke3Wtf0jFR1Iy55x0LIYEMGQk5xawg8w0udGV7pypFx4LAJMd4wNY4TdbFRAxEHG3WKIaVT6NNaovc8iPfKSkh4pK68iPzs6QdQvYiIiIiIiAwXBZYicmjpHmno9QbFPVGeAacuig3lBhWGFgLckQllhxLy19aW07SjozCSdJcRr/0CzuLo11gixJee/jEbUtuZGq7js3PfR8yJ9Btd2zMKtypeGIW7ax1m9zoqK+O0tSd75j+1TjGo7741vc++caBmQoKmtq7CXKTdt98P8fNI5gO+/UojzzX5UO9wzWGVvPmEqTQ1deLv5bML/+1pQq+sxBpD+NrL6ZwwQXNHioiIiIiIjBEKLEVExjljzB4Dv10zuICAf93wSx7xllNRXca/HPlOItEKBrrpeyijcIc6YhcDJuFBxtnvoLAhleffXtrJxq4cYcfwySNqecPEeOHz2IvQ88uJ/P15ADIXn01s0Vxo7Ni/IkRERERERGTYKbAUETmE/O/mJfx12zN4xuX/m///mBKt3/eLxqDXWjN8fdlO2nIB1WGXLx1Tz2EV+54l1Fu5jsjSRwHInHEi+WMXjnSpIiIiIiIiMkQKLEVEDhGPNr3AbZuXAPCx2VdxVMVhJa5o/zyyvYsfvNZELoA5iRBfPLqeuui+/+/M2bKd6F1/xVhL9piFZM84cVCLK4mIiIiIiMjoOpD1EUREZJzYnm7kP9feBsB75lzERRNPL3FFQxdYy21rWvnu8kJYeUp9jK+fMHFQYaVpaiX2+yWYfJ783BlkLjqrcB+7iIiIiIiI9GhpaeGqq67g+eef7Tn2hz/8jne+8y0sXnwm73znW/jDH3474nVohKWIyEEuH/h8Y/WtJP00C8tn8bGFb6etOVXqsoYkkw/49itNPNaQBOCtMyu4em4lziBCR9OVJP67e3BSafxJ9aSuuAAc/b1ORERERESkr2XLXuRrX/syW7Zs7jn22GN/45Zbfsx//ufNHH74Ql57bTkf/eiHmT17Lscff+KI1aLAUkTkIPe/W+5lRed64m6Uz897PyFnfP3ob8n4fP7B9bzanMIzcP3hNZw3pXxwL87miP3uXpzWDoKqClLvuATCoZEtWEREREREpA9rIZMfXFsfyA+y7d6E3aHdVLZkyT3ccsuPuf76G7jxxi/0HD/jjLP4wx/uJh4vI5/P09raijFQXj7I38n20/j6rVVERIbkpbaV/HbLUgA+PvtdTIrWlriioVnfmeWrL+6kMeOTCDn881F1HFEdHdyLfZ/YHffhbt9JEIuSvOpSbFl8ZAsWERERERHpw1r45mOwpnkod3kd+B1hc6stnzrNDjq0PPnkU1m8+CI8z+sXWALE42Vs3Lieq6++Ct/3ueqq9zB//uEHXOPeKLAUETlIteU6+Y/V/4PFcmH9aZxdd0KpSxqS55tS/MfLjaR8y4xEmC8cVcvk2CBHR1pL9C9/w1u7Cet5pN5+CbamakTrFRERERERGch4mD2/trZur+enTJnGAw88zurVK/n85z9FdXU1733vtSNWjwJLEZGDkLWW7679X5pybUyLTuQjs64sdUlDsmRzBz9d2UJg4ajqCN86dzbZ9iTWDu714UefIbRsBdYYUlcsJpg6cWQLFhERERERGYAx8NkzIJkJBtXe8xzy+cG13Zuh3hK+L55XiBAPP3wRb3/7O1m69C8KLEVEZGjubvgbT7W8Qsh4/PO89xN1I6UuaVB8a/mf1a3ctbEDgDdOLuOjC2uoCLs0DrKP/BMvEX7sOQAyF56FP2/WyBQrIiIiIiIyCMZAZJAJnOeBO7LlDMlvf3sby5e/wle+8vWeY7lcjoqKihG9rpZJFRE5yKzt2szPNtwBwAdnXMHcsmklrmhw0n7AN19u7Akr3zOnkhsW1hByBv9nQXfNBvJ/KMzZmTn9BHLHLRqRWkVERERERA4FxxxzPI8++jAPPHA/QRCwbNmL/P73v+aKK0b2Lj6NsBQROYik/QxfX/ULcjbPyVVHcPmks0td0qA0Z3z+7aWdrOnIEnLghoW1nDWpbEh9OE0tRO/8KwSW3NELyJ510ghVKyIiIiIicmg4/PCFfPWr3+RnP/sR3/zmvzFp0iQ+/vFPc955i0f0ugosRUQOIj/Z8Ac2pRuoCVXwT3PfixnOSUtGyPqOLF99qbASeEXI4QtH17Owaoi3sKczRG//CyaTxcyeRubis4d3whYREREREZFDxGOPPdvv+RlnnMUZZ5w1qjUosBQROUj8rel5luz4OwbDZw57H1WhRKlL2qfnGntXAp8a9/j/jqlncnyQK4F3s5bY3Q/gNrcSJMqIXXs5ZAIY5AI9IiIiIiIiMrYosBQROQg0ZJr53tpfA/COKYs5rnJBiSvatz+uaua7L+0ksHBkVYR/Prqe8tDQp1YO/+1pvNUbsJ5L+sqLiSfKINMxAhWLiIiIiIjIaFBgKSIyzuUDn2+supUuP8Xh5bO4etqlpS5pr3xr+Z9V/VcCv/7woS2u081bsYbI358HIH3xOQST64e1VhERERERERl9CixFRMa5n628i1c71hJ3o3z+sGvxHLfUJe1RKh/w3eVNPN2YAuC9cyu5cmbFfs216exoInrPgwBkTz6G/JHz0ayVIiIiIiIi458CSxGRceyltpX8fNWfALhh9juZFK0rcUV71pjO828v7WRdZ46QA186ZRrHlTnY/ZlrMpkm9oclmFye/KxpZM49ddjrFRERERERkdIY+mRhIiIyJnTlU3xr9a+wWC6sP41z6k4sdUl7tKo9w6efaWBdZ47KkMPXTpjI4pmV+9dZEBC7cylOawdBVQWpKxaDo/87ExEREREROVhohKWIyDj13xvvYme2hanxej4y+8pSl7NHjzckuenVJrKBZWZZiC8dU8/E+P7/30/kwSfwNmzBhjxSV14EsegwVisiIiIiIiKlpsBSRGQceqltJX/e8RgA/98xHyRGZP9urR5B1lr+sKGdX61pA+CE2iifPrKOuLf/oyG9l18n/MwyANKXnUdQXzsstYqIiIiIiMjYocBSRGScSftZblr7fwBcMuENnFS3kMbGjhJX1V8usNz8WjMPbe8C4LLpCd5/WBXufqwE3s3Z2kB0ySMAZN5wAvkFc4alVhERERERERlbFFiKiIwzv9r8Z7ZlGqkLV/GhmVeUupzdtGd9vr6skVfbMjgGPjy/mounJQ6oT9veSfQP92F8n9y8WWTPPGmYqhUREREREZGxRqsUiIiMI693buCObQ8C8LHZ76TMi5W4ov42deX4zLMNvNqWIe4a/uWY+gMOK/F9srfehdPRhV9bTfqy88Ds/0hNERERERERGVhLSwtXXXUFzz//7G7nGhsbueyyC7j33rtHvA6NsBQRGSdyQZ7/XPO/BFjOrTuJU6qPLHVJ/Ty9vZMvPrOdrrxlYtTlS8dMYEZ56ID7jSx9DLt+CzYSJvW2iyASHoZqRURERERERoe1kM8PtjHk/QO/pusOfZzHsmUv8rWvfZktWzbvdi4IAr7ylS/R1tZ64MUNggJLEZFx4jdb7mN9ahuVXjnXzXxbqcvpYa1lyeZOfrayBd/CosoInz+6jsqwe8B9e8tWEHrhVTCQvuJ8bG3VgRcsIiIiIiIySqyFB/8CTTuHcpPzgd8QXVtvOet8O+jQcsmSe7jllh9z/fU3cOONX9jt/C9+8TPq6ycwYcLEA65tMHRLuIjIOLAuuYXfbL0PgI/OfgeVofISV1SQCyw/XNHMj18vhJXnTorzleMnDEtY6exoInrfowB4F56BP3fmAfcpIiIiIiIy2sbDhFYnn3wqv/3tnZx33gW7nXv++Wd54IGlfOpTnx+1ejTCUkRkjPOtz3fX3IZvA06vPpoza44rdUkAtGR8vvHyTla0ZTHAdcdM4KK6CMPyf8eZLLE77sPk8+TnTCdy/mnQ3Hng/YqIiIiIiIwiY+DciyCTCQbV3nMd8v7g2u7NUG8Jr62tG/B4S0sz//7v/8q//dt/EI/HD7iuwVJgKSIyxt2x7SFWdW2k3I3x0dlXYcbAgjOr2jN8fVkjTRmfMs/w6SPruPDwehobO7D2ADu3luiSh3Ga2wgSZaTffB7lTunfs4iIiIiIyP4wBrxBJnCex5gZkmmt5atf/ReuvPIqDj984aheW4GliMgYtiW1g19u+jMAH575VmrDlSWuCB7a1sXNK5rIBTAt7vGFY+qZVnbgi+t0Cz33CqHX1mAdh9RbLoD42FoJXURERERE5FDQ0NDAiy8+z6uvvsKtt94CQFdXF9/5zjd4+OEH+I//uGnErq3AUkRkjApswE1r/4+szXF85eEsrj+1pPX4geV/1rRy18YOAE6qi/FPR9QS94ZvOmRnSwORB/4OQOaNpxFMnTRW/rgoIiIiIiJySJk0aRIPPvj3fseuvPIyPvCBD3PJJZeN6LUVWIqIjFF/bniclztWE3XC3DD7XSW9Fbwj5/PtV5p4sTkNwDtmVfCuOZU4w1lTMk3szqWYICB3+BxyJx41fH2LiIiIiIjIuKHAUkRkDNqWbOLnG+4E4P0z3sykaG3JatnQmeVrLzWyPZUn4hg+vqiWN0wc5smWrSV29wM47Z0E1ZWkLzl3aDNEi4iIiIiIyLB47LFn93ju9tvvHpUaFFiKiIwx1lr+fdkvSAUZFiXmcNnEs0pWyyOb2/nXZxpI+5YJUZcvHl3PrER42K8TfuJ5vLUbsZ5L6q0XQmT4ryEiIiIiIiLjgwJLEZEx5oHGZ/j7zpcJGY9PznkPjhm+OSIHK7CW365t4zfr2gE4qjrCZ4+soyLsDvu13A1bCP/tGQDSF55FMKF0o0lFRERERESk9BRYioiMIS3Zdn68/nYArp5+CdNjE0e9hrasz38ub+KF4nyVl00v59rDqvGc4b9F23R0Eb3zfoy15I4+nPzRhw/7NURERERERGR8UWApIjKG/HzjnXTkkyyomMmVU84b9eu/1prhW6800pTxCTuGz540hZMTLtYO/7WsHxC9836cZAp/Qi3pC84Y/ouIiIiIiIjIuKPAUkRkjHilfTV/bXwag+GLx1yLmx+ZoHAg1lru3NjBL9e0EliYGvf4/NF1nDCrisbGjhG5Zn7Jo7ibtmHDIVJvuQBCoRG5joiIiIiIiIwvCixFRMaAfODzX+t+C8DFE0/niKo5IxYU7qoj5/O9V5t5pjEFwFkT41x/eA3x0MjNnemuXIf/4FMApC89F1tTNWLXEhERERERkfFFgaWIyBhwV8PDrE9to9Ir5/3T3zxq113ZVrgFfEfaJ+TAh+ZVc+HUcowZ/vkqu5nWdqL3PAhA9qSjyB8+d8SuJSIiIiIiIuOPAksRkRJrzLbyv5vuBeADMy6nIlQ24te01nLPpk5+saqFvIVJMY/PHVXHnER4ZC/s+8TuvB+TzmJmTCb7xtNG9noiIiIiIiIy7iiwFBEpsZ9t+COpIMPC8tksrj9lxK/XmfX55suN/H1H4Rbw0+pjfGxRLWXeyN0C3i3y0JO423ZgoxEi77scfAOjNE+niIiIiIiIjA8j/9upiIjs0Qttr/NI0/M4GP5x9lU4ZmR/LK/tyPKBpWv5+44UnoEPza/mc0fVjUpY6a1cR/iZZQCkL3sjprpixK8pIiIiIiIig9fS0sJVV13B888/23Ps29/+OueeexqLF5/Z87jrrj+OaB0aYSkiUiLZIMfN634HwJsmncXcsmkjdq3AWpZs7uQXq1vIBVAfdfnMkXUsqIyM2DX7Mq3tRP9cnLfy5GPw580aleuKiIiIiIjI4Cxb9iJf+9qX2bJlc7/jr732Kp/97Be5+OI3jVotCixFRErkj9seZHO6gepQgvdNG7kf/E3pPN9/rZkXm9MAnD6lnI/Oq6Tcc0fsmv30mbfSnzKBzDmnMHJL+oiIiIiIiIwt1kKQG1zbwEKQP/BrGg+GspbqkiX3cMstP+b662/gxhu/0HM8m82ydu1qFixYeOBFDYECSxGREmjINPPrLX8B4EMz3kKZFxuR6/xtexc/fr2Zrrwl7BiunVfFNcdOprmpEztKc0dGHn6qOG9lmNQVF4A7SkGpiIiIiIhIiVkL638HqW1DmYbrwKfsik62TH+rHXRoefLJp7J48UV4ntcvsFy9eiX5fJ6f//zHLFv2EmVl5bzpTW/m3e++BscZuanFFFiKiJTAT9bfTibIcVTiMN5Yd9Kw99+R8/nJ6y082pAEYF5FmE8sqmV6eQhnKH9mO0DeynWEn34JgNSb3oitTIzatUVERERERMaEcXCLWW1t3YDHu7o6Oe64E7jyynfyr//6dVauXMEXvvAZHMfh3e++ZsTqUWApIjLKnm5Zzt9bluEah4/OvgozzAHiC00pvv9aM80ZH8fAO2ZV8PZZlXjO6P6/pGnr6J238qSj8efNHtXri4iIiIiIlJoxMOvtkEsHg2rveQ75/ODa7vW6Q7wlfE9OOulUTjrp1J7nixYdyTve8S4eeOB+BZYiIgeLTJDlR+t/D8AVk85lVnzy8PXtB9y6upV7N3cCMCXu8clFtcwfpYV1+vF9YncuLcxbOXkCmXNP3fdrREREREREDkLGgBMaXFvHg1Eea7JXf/vbwzQ3N3HFFW/rOZbNZolERvb3TAWWIiKj6Hdb7mdbppG6cBXvmXbxsPW7qi3Dd5Y3sTVZmJ350mnlvO+wKiLuyM0psjeRh5/C3do9b+VizVspIiIiIiIyDllr+cEPvsu0adM54YSTWL78ZW6//Td87GP/NKLXVWApIjJKtqZ38rut9wPw4ZlvJe5GD7jPfGC55eUd3PrqTgILNRGXGxbWcFztyCziMxjuqvU981amL30jtqqiZLWIiIiIiIjI/jv77HP52Mf+ie985xvs3LmDmppaPvCBf+DCCy8Z0esqsBQRGQXWWn647vfkbJ7jKhdwZs1xB9zn6vYsP1zRxJqOHABnTIhz3eHVJEKlG81o2jqI3dM7b2V+vuatFBERERERGU8ee+zZfs+vuOJt/W4JHw0KLEVERsHD25/nmdZX8YzL9bPecUAL7STzAbetKcxVGQCJkMOHF1Rz1sSy4St4P1jfJ3rn/Zh0RvNWioiIiIiIyH5TYCkiMsLSfoZvL78NgCsnn8f02MT96sday993pLhlVQvNGR+AsyfF+fQp07BdaawdtpL3S/7eR3G3NGAjmrdSRERERERE9p8CSxGREfbrLfexPdXExEgN75x60X710ZDK85PXm3muKQ3A5JjHdQuqOa4uRm0sRGNXejhLHjJ31Xr8h54GIH3puZq3UkRERERERPabAksRkRG0Nb2TP2wtzOl43ay3EXXDQ3p9PrDctbGD36xrIxtYPANvnVnBlbMqSrYC+K5MWwfRu4vzVp54FPkFc0pckYiIiIiIiIxnCixFREbQTzf8kZzNc0rdEZxWffSQXvtqa4YfvdbMhq7CojpHVkX4yOE1TCsLjUSp+yfvE7tjKSadwUyfRPaNp5W6IhERERERERnnFFiKiIyQ51pf48mWl3Fw+PSR78FkzKDmmezI+dzy9Fb+tLYFKCyq84F5VZw7qeyAFusZCZEH/467bQc2GiHyvsshcKDEc2mKiIiIiIjI+KbAUkRkBOQDn59s+AMAl08+mzmJqTRmOvbxGsvSrZ38Zm0bbbkAgPMnl/G+eVVUhMbeAjbeq6sJP/cKAOk3n0esphIa9/4eRURERERERPZFgaWIyAi4u+FvbExtp9Ir573TLt5rW2stT+5M8cvVrWxN5QGYVRHhH+ZVsqgqOhrlDpnT1EJ0ycMAZE4/Hv+wmaUtSERERERERA4aCixFRIZZa66D/918LwDXTr+Mci++x7avtWb4xeoWXm/LAlAZcnjnnErec8xkWps7B3UL+ajL5oj+8T5MNkd+xhSyZ57E2LpRXURERERERMYzBZYiIsPsfzbdTZefYm58GhdMGHgRms1dOX61ppUnd6YAiDiGy2ckeMvMCspCDp4zRiNAa4ne9zfcxhaCsjjpyxeDMzZWKxcREREREZGDgwJLEZFhtKprE3/Z8QQAH5n1dlzTP8xryfj8Zl0bS7d2ElhwgPOnlPHOOZXURsb+j+TQS68RemUl1hjSVyzGlu959KiIiIiIiIiMLy0tLVx33fv53Oe+xPHHnwjA6tWr+MEPvsurry4nGo1ywQUX8ZGP3IDnjdzvsGP/t2MRkXHCWsuP1v8ei+Wc2hM4smJuz7lkzufXa9u4Y0M7ab9wn/fJdTGunlvFjPJQqUoeEmf7TiJLHwMge/Yp+DOmlLgiERERERERGS7Llr3I1772ZbZs2dxzrLW1lU984iNcddV7+M53fsDOnTv45Cf/kdraet797qtHrBYFliIiw+Thpud4tWMtESfMB2dcAUDaD7h/ayd/3LiV5nRhQZ15FWHef1gVR1SPzQV1BpTOELtjKcb3yR82k+ypx5a6IhERERERkfHBAtlBtvWLjwMVgqEsNrBkyT3ccsuPuf76G7jxxi/0Oz59+gyuvvr9AEyePIWbbrp5aJ3vBwWWIiLDIO1n+PnGOwG4asoFxJxKfruujbs3ddCRCwCYFPO4Zm4Vp0+IYcwYnaNyINYS/fNDOK3tBJUJUm96I4yn+kVERERERErFAt+H0PrBz/0f4sDXCQhmWfzr7aBzxZNPPpXFiy/C87x+geVrry1n9uy5fOtb/86jjz5CNBrl0kvf3BNgjhQFliIiw+A3W5fSmG2lPjSdZPJEPvj4lp5bvyfFPK45op5TKjy8cRj0hZ5eRmjlOqzrkHrLBRAbRyNDRURERERESm0c/BpYW1s34PH29jb+9reH+PSn/5lPfvKzrFu3ls997pOEQmHdEi4iMpZtSzfyh83P4WUvpCt5NHe3JgGYVR7ibTMrOGNinIkTKmhs7MDaEhc7RMG6zYQfLCwilDnvDQSTJ5S4IhERERERkXHEAB+DXCoYVHPPdcj7g2u7V0O8JXxPwuEwCxcewZvedDkA8+bN58orr+Khh+5XYCkiMlat7cjyr8vWYFLX4uDgA4sqI7xtVgUn1EYxxozbu6dNV5LsL/+EsZbcosPIHX9EqUsSEREREREZfwwQHmRbD8iPYC1DNGvWbJ5//rl+x3w/GPHBOAd+U7yIyCHGWssrLWk+9cgGPvHUdlpSkzE4LKoyfP2ECXz9xImcWDfO5qncVRAQuesBaOskqK0iffE5mrdSRERERETkEHPppZezdu1qbrvtf/B9nzVrVvPHP/6OCy+8ZESvqxGWIiKD1JHzeWhbF/dt6WRzsvtPXgG++zpnTM7z+QWXlrS+4RR+5Cm89ZshHCL91gshHCp1SSIiIiIiIjLKZs6cxQ9+8FN++MPv8atf3Uo0GuWKK97GlVdeNaLXVWApIrIX1lpWtGW5b0snj+9Ikg0K496jruGw2k6e67iNinCOj869scSVDh9v+UoiT74IQOiqiwjqawor24mIiIiIiMhB77HHnu33/IgjjuTmm382qjUosBQRGUBnLuCR7YXRlBu6cj3HZ5eHuHBqOcfXwT8u/wE4Sd43/SoSXryE1Q4fZ9tOovc+DED2tOOIHrcQGjtKW5SIiIiIiIgcUhRYiogUWWtZ3pTkN8ubeLShdzRl2DGcOTHOhVPLmV8RxhjDD9b9ho5ckjnxqVw04Q0lrnx4mK4ksT/+BZP3yc+dQfbsk0tdkoiIiIiIiByCFFiKyCFvZzrPYw1JHtnexbrO3tGUM8oKoynPmVRGeah3jbK1XZtZ0vA4ANfPfjuuOQjWL/N9oncsxWnvJKipJPXm8zHOQfC+REREREREZNxRYCkih6SmTJ6/NyR5dEeS19uyPcfDjuENE2JcODXB4ZXh3Vb6ttbyo/W3E2BZPOVkjqo4DHsQzO8Yuf8xvE3bsJEwqSsvhmik1CWJiIiIiIjIIUqBpYgcMlozPo/vSPL4jiSvtmZ61pExwBFVEc6YFOeKhRPIdqT2GEI+2vwCL3esJuKE+PjCd0JytKofOaEXlhN+4VUskHrz+QS11aUuSURERERERA5hCixF5KDWmsnzl82dPNbQxSstGYI+5w6vDHPGxDJOnxCjNuJhDFREvD2uMZP2s/xswx0AvGPKYibHa2lMju8FadxN24gsfQyA7Nkn4x82s8QViYiIiIiIyKFOgaWIHFSstWzsyvFcU5rnm1K82prB7zNacl5FmDMmxHnDxDj10aH9CPz91vvZmW1hQriaK6ecP8yVjz7T3kn0j/dhgoDc4XPJnnZ8qUsSERERERERUWApIuNfVz7gpeZCQPl8U5qmjN/v/OzyEGdMjHPGxDImxfbvx15Dppnfb/0rAB+a+RaibviA6y6pXJ7YH5bgJFP4E2pJX3ou7DJfp4iIiIiIiEgpKLAUkXHHWsvajizPNRYCyhVt/UdRhh3DUdURjq+Ncf68WmKZ7AEvjPOzDXeQtTmOShzGmTXHHVhnJWatJXLvw7jbGwliUVJvuwjCoVKXJSIiIiIiIgIosBSRccBay5ZkntfaMixvzbCsZStN6Xy/NlPjHsfXxji+NsoRVREiroMxUJeI0JjJ7qHnwXmpbSWPNb+Ag+Ejs96+28rh443/8DOElq/CGkP6LRdgqypKXZKIiIiIiIhIDwWWIjLm5APLmo4sr7VmeLUtw2utGdpzQb82EcdwdE2U42ujHF8b2+9bvffFtz4/2nA7AJdMPIM5ZVNH5DqjxV27kfw9jwCQOf8N+DPH9/sRERERERGR4dPS0sJ1172fz33uSxx//Il861v/ztKlS/q1yWQynHjiyXz3u/81YnWMm8By8+bNfOMb3+DZZ5/FWssJJ5zAP//zPzN9+nQA1q1bx5e//GWWLVtGWVkZ733ve7nuuut6Xv/II4/w7W9/m02bNjF58mQ++9nPcu6555bq7YhIH105nxeaUixvyfBaW4bX27Jkg/73cIcdw7yKMEdURXjDrGqmOQHeKIx0vLfhcdYnt1Luxrlm2qUjfr2RZJpbid55P1hL7pjDyZ1wZKlLEhEREREROfhZC9lg3+0AAiA/yLZ7EzJDXqdg2bIX+drXvsyWLZt7jn3mM1/gM5/5Qs/zp59+ki9/+Yv84z9+8sBr3ItxE1h+9KMf5cgjj+TBBx/EWsvXvvY1rr/+eu6++25yuRzXXXcdixcv5mc/+xmrV6/mH/7hH5g5cyYXX3wx69ev52Mf+xjf/e53Oeecc1i6dCmf+MQnWLp0KRMnTiz1WxM5pHTmAtZ1ZlnbkWVNR5a1HTm2JHPskk+S8BwWVkVYWBVhUVWEuYkwIccUbvOuK6exseOA56Xcl458F7/cfA8A10y/lIpQ+checASZriTx3/4Zk85iZk4hc+FZWmRHRERERERkpFlL6EdbiWxID/olkWG4rD8zQvr/TRn0731LltzDLbf8mOuvv4Ebb/zCgG1aW1v5yle+xCc+8WnmzJk7DFXu2bgILNva2qirq+PjH/848XgcgGuuuYbLL7+ctrY2li9fzo4dO7jhhhsIh8MsWrSIq6++mttuu42LL76YO+64gxNPPJHzzz8fgEsuuYQ//vGP/Pa3v+WGG24o5VsTOai1Zn3WtHcHk4VwcnsqP2DbiTGXhZURFlVFWVgZYVqZh1PiQO2Xm/5MRz7JrPgULp14RklrOSDZHLHf3YvT2k5QlSD2/isga2GEA18REREREREBxsFYkZNPPpXFiy/C87w9BpY/+tH3WbBgERdccPGI1zNmAst0Ok1DQ8OA5+rr6/n5z3/e79h9993H1KlTqaysZNWqVcyePZtwONxz/rDDDuOnP/0pAKtXr2b+/Pn9Xn/YYYexYsWKIdd5sA5I6n5fg3l/Q2k7XvtWHUNr35712ZzMsakrz+auHJu7cmxMbmXnHsLJCVGXOYkwcxNh5laEOXFmNSaZHtSIydF6j+u6tvDnhkcB+MisK/EctyR1HHBb3yf6x/twt+/ExqKk3/Um4hXlmKaO0a1jlPoeK3WMZN9jpY6R7Ft1jF7fY6WOkex7rNQxkn2rjtHre6zUMZJ9q47R63us1DGSfauO0et7rNQxkn2PlTr21n7A1xtD7rqp5FP+oPr2PId8CW4Jr62t2+v5rVu3cN999/Kzn/3PoPs0A5Qw6M/Y2pG+qXJwnnrqKa655poBz9188809oyMBfv3rX/ONb3yDH/3oR5x++un88Ic/5PHHH+e2227rafPEE0/wwQ9+kFdffZVrr72W4447jo9//OM952+66SZefPFFbr311hF7TyIHk8BadiRzrG/PsqE9w/riY0N7htbMwD94DTA9EWZBdYwFNVHmV0WZXx2lIjJm/lYyIGst1z3xTZ5teo3zJp/If5z4sVKXtF+steR+fS/Bs8shHCL8kXfizJxc6rJEREREREQOSul0mjVr1lJXN4lweDhu7C6NU089nptv/iknnHBiz7Gf/OSHvPTSi/zwhz/d5+uz2QyNjduZO3cO0Wh0v2oYM6nBKaecwuuvv77XNtlslq9//evce++9/OQnP+HUU08FIB6Pk0ql+rVNpVKUlZUBEIvFSKf7zxWQTqd7zg9FU9PIz5tXCsZAbW1iUO9vKG3Ha9+Hah2pfEBDOs/2ZJ6GdJ4dqTzbU3kaUj4NqTyZXSea7KM+6jKtLMT0eIjp5SGOmlJJTZAj6jp9WlmyHSka+wzuGyufdd/2d616lGebXiNsQlwz6TIaG3cfjTiWv47dwg89SfjZ5VhjSF+xmM6ywsjKg+F7dazXMZJ9j5U6RrJv1TF6fY+VOkay77FSx0j2rTpGr++xUsdI9q06Rq/vsVLHSPatOkav77FSx0j2PVbq2Fv7XC5LEAT4vu03SnIooyaHbYTlAfL9oF8dDz30AO9853sHVZvvW4IgoKWli1Ao1+9c92e3L2MmsNyX5uZmPvKRj5DNZrn99tt7VgcHmDdvHuvXryefz+N5hbe0evVq5s2bB8D8+fNZvnx5v/5Wr17NkUcOfYVcazkoA8tuQ3l/Q/0sxmPfB1MdgbW0ZQOaMj5NmTzNWZ+uzUnWNyeLoWSe9tzef/C4BqbEPabFQ0wrC/UElFPiHjGvN5g0Burq4kNaGGesfNZpP8tP198BwJVTzmNipHavrx2r30+hZ5YRfuIFANKXnEN+7sx+c1aO5e/Vg6mOkex7rNQxkn2rjtHre6zUMZJ9j5U6RrJv1TF6fY+VOkayb9Uxen2PlTpGsm/VMXp9j5U6RrLvsVLHQO2H8trxpK2tlfXr13HssccP6XVD/Tz7GheBZS6X40Mf+hDV1dXcfPPNuw0nPeWUU6iuruY73/kOn/jEJ1i3bh2/+tWv+OQnPwnAm9/8Zn7xi19w7733csEFF7B06VKefvppvvjFL5bi7YgMK99aOnMBbVmftlxAvtNnXWMXTek8TRmf5oxPU8anJevjD+IHRcJzmBjzig+XCVGPSTGPSXGPRdOqaW3uPGh/CAP8as0SGjLN1IWreMeUxaUuZ794r60m8tfHAcicdTL5ow8vcUUiIiIiIiIyXm3bthUorDEzWsZFYPnQQw+xfPlyIpEIp512Wr9zf/7zn5kyZQr//d//zVe+8hXe8IY3EI/Hufrqq3nrW98KwNy5c7n55pv59re/zRe/+EWmTp3KD37wA2bPnl2KtyOyVxk/oDMf0JkrbE3asrmpi9aM3xNKtmV92rIBrVmfjlzAYAeLO0BVxKW2+JhWFaOCoCeUnBjziHvOgK81Bjxn8BP2jkc7Mi38YtU9AHxoxhVE3fE354i7YQvRux/AANnjjyB7+tD+AiYiIiIiIiKHtscee7bf88MPX7TbsZE2LgLLCy64YJ/zW86cOXO3lcT7OvPMMznzzDOHuzSRfgJrSeUtyXxAMm9J+cFu+ynf4m/qorE9TUeuN5zsyAV05QOye5kncm8SnkNF2GFieZiEA7URryeYrCluq8MubjF0LNy2nRjSbdsHu59vuJNMkOXIxFzOrj2h1OUMmbOjidgf/oLxA3ILZpNZfMaQVoUTERERERERGQvGRWApMhz8wJLM+bRnfdK+JRtYcr4lE1hyQeF5xu/dD7dkaWxLk/YDMr4l5VsyfkDat6T9Qtvuc+mgcCw5TBPjOgbKPYfykEN1LESZgYqwQ1XIpTLsUBF2qQo5VIZdKsMuFSEHzzEKIQ/AK+2rebjpOQyGj8y+EjPOgj7T1kHst3/GZLLkp00ifdn54Aw8WlZERERERERkLFNgKUBhZGBrJk9r1scPwFpLAAQWLIXnfvc+llYvTVN7Ft9aAkvxUWgTFPvrPmaBspSltS1JPijMuehbel676/PIthTtXRnyQeF4PrDkbe++byFvbc++cRtJZvPkAksuKLTJFR9523tsNNfYcg3EPYe4a4h7DrE++3HPoTYRwcv7lHlOTzCZCDmUeYVtzDUYowBytPjW57/W/w6At8w4m8PKpo+rz9t2pYj95s84nV34ddWkrrwEQvrxLiIiIiIiIuOTfqMVrLV8/rkGVrRlS13KqPEMhB1D2DWFbXE/5Bgixf3yaBjj+0QdQ9Q1RF2HiFvYjxSfR91C+5jnMLW+nHRHipjjEHLY4wg9hZBjz13bH2F9cisVXhkfXfh28u3j6AuTy5P9vz/hNLUQJMpIXfUmiI2/uTdFREREREREuimwFABibu+tow6FW5KNAYPBMYVjxoBjDA7gug5YW9jvPt53S+++ayAS9gjyPq4xPe1dQ8/znq1jKIuFyGXyuAY8Y3CdwtZzTO+x4gIwnmOoqYyR6kwTcgyeKYSOngMhx/Q7FnZhcn0FbS2dOOz9dt+hhorGQF0iQmMmqxBynGnKtvK/m+8F4AMzLqcqXE4jHSWuapDyPtE7l2LXb8FGw6SuuhRbUV7qqkREREREREQOiAJLwRjDvx4/gZracpqbOvcZuO1XmDfI9vvftzOovqOeQ6cxChWlx0833EHST3N4+SwunHBqqcsZvFye2B/vw1u7ETyX1JUXE9TXlroqERERERERkQOmwFJ6OONskRGRA/VC2+s80vQcDoaPzr4Kx4yTRWqyOWJ/+Ave+s1YzyP8obcS1NQWJpkVERERERERGefGyW/nIiLDKxfk+eG6wkI7l048k3ll00tc0SBlssR+9+dCWBkOkXrnpbjzZ5W6KhEREREREZFhoxGWInJIumPbQ2xKN1AVSvC+6W8qdTmDk84Q/+2fcbc2YCNhku+4FDt9UqmrEhERERERERlWGmEpIoecHZlmbtuyBIAPzbiCci9e4ooGIZUm/uu7C2FlNELyXZcRTFNYKSIiIiIiIsOnpaWFq666gueff7bn2F//eh/vec+VXHDB2bzznW/lzjtvH/E6NMJSRA45P9nwBzJBliMTczmv7uRSl7NPJpki9uu7cXc0EcSipN51GcHEulKXJSIiIiIiIgeRZcte5Gtf+zJbtmzuObZ27Wq+8Y2vctNNP+LII4/i5Zdf4oYbrmP27Lkcc8xxI1aLRliKyCHl2dZXebz5JRwcPjr7HZgxvtiU6ewidttdhbCyLEbqPZcrrBQRERERERlPrIWsP7oPO7RVWZcsuYd//dcv8eEPX9/v+KZNG/F9H2sDrLUYY3Ach3A4PJyf0G40wlJEDhnZIMfNxYV2rph8DrPjU0tc0d6Z9k5it/0Jp6WNIFFG8l1vxtZWlbosERERERERGSxrCd/yLNGNbaN6WX96Jan3nwCDHKRz8smnsnjxRXiex403fqHP8dM44oij+MhHPojruvi+z0c/+gkWLjxipEoHFFiKyCHk91v/yrZMIzWhCt4z9eJSl7NXQXMbsf+9E6e1g6AyUQgrqytKXZaIiIiIiIgM2di+sw+gtnbgO/lyuSyTJ0/hfe/7IMcddwJPP/0kN974z8ydexgnn3zqiNWjwFJEDgnb0438dstSAD48862UebESV7RnprmN7G/vKYSVVRUk3/1mbGWi1GWJiIiIiIjIUBlD9kMnkk/lB9Xc8xzy+eDArxtyBj26cm9+/vOfEA6HOemkUwA4/fQzOP/8C7nrrj8osBQROVA/Wn87WZvj2Ir5nF17QqnL2SNn+05iv18CnV0ENVUk330ZNlFe6rJERERERERkfxkDYXdwbT0HnLEzIrOhYTsVFZX9jrmuh+eFRvS6WnRHRA56TzQv46nWV/CMO6YX2vFeXUX8V3fidHZhJtWReu/lCitFRERERESkZM444yweeGApTz31BNZaXnjhOZYuXcIFF4zsNGsaYSkiB7VUPsOP1t8OwFsnv5HpsUklrmgAQUD4kaeJPPkCAPk50yn74FuwXTkY2sJuIiIiIiIiIsPmTW+6gnQ6zU03fYumpiYmTpzIpz/9ed7whjNH9LoKLEXkoPaL1XfTkGlmQriad0+9qNTl7C6dIXbXX/HWbgQgc+px5M45mfJYFLpyJS5OREREREREDjWPPfZsv+dXXvlOrrzynaNagwJLETlobU418Ms1SwD4h1lXEnUjJa6oP6exhdjtS3Ba2rCeR/qSc8gfMW845kUWERERERERGbcUWIrIQclay83rfk8uyHNS1SJOrz661CX1465aT+xPf8VkcwQV5aTedhHBpPpSlyUiIiIiIiJScgosReSgdP/OJ3m+bQURJ8RHZr197Cy0Yy3hvz9P+G9PY4D89Mmk33IBtixe6spERERERERExgQFliJy0GnKtvLTDX8E4LoFb2VqrB47FhavyeaI3vMgoRVrC0+PP4LM+W8A1y1xYSIiIiIiIiJjhwJLETmoWGv5r3W/o9NPMb9sBu+ecyGtzclSl0XQ1Ersl3fg7mjCOg6ZC88kd+yiUpclIiIiIiIiw8yOiREzpTMc71+BpYgcVP7W9DxPtCzDMy7/NPc9eE7pRy+6azaQvech3K4UQVmM9FsvxJ82udRliYiIiIiIyDByi3fPZbMZwuGxtejraMpmMwC47v7HjgosReSg0Zbr5Ifrfw/AVVMuYHbZ1NIWlEoT/evjhF5ZCYA/eQKpt16IrSgvbV0iIiIiIiIy7BzHJRYrp7OzBYBwOIIxhiAw+P7gRh0Ope1YY60lm83Q2dlCLFaO4zj73ZcCSxE5aPx4/e205TuZFZvMO6deWNJavBVriNz3KE4yhQW8s06g85TjsZ5+7IqIiIiIiBysKipqAHpCSwDHcQiCYFCvH0rbsSoWK+/5HPaXfnMWkYPCky0v81DTszgYPjn3vYSc0vx4M51dRJY+Suj1dQD4ddVkLj2HmmPmQWMHjM8/lImIiIiIiMggGGOorKwlkajG9/MYA9XVZbS0dO1zMdihtB2rXNc7oJGV3RRYisi415lP8oO1vwHgrZPPY0H5zNEvwlq8l18n+sDjmHQW6zhkTzuO7OknYEKln0dTRERERERERo/jODhOGGMgGo0SCuUGFVgOtu3BToGliIx7t2y8k6ZcG1Oj9Vw9/ZJRv75pbSe65BG89ZsB8CfVk770XIIJtaNei4iIiIiIiMh4p8BSRMa1F9pe5y87/g7AJ+a8h4gTHrVr28ASeuZlwg8/icnlsZ5L9syTyJ58DAzDEHgRERERERGRQ5ECSxEZt9J+hu+t/T8A3jTxTI6qOGzUrm0aW8j++k9E1m0BID99MumLz8HWVo1aDSIiIiIiIiIHIwWWIjJu3brpbrZnmpgQruYDMy4flWua1nbCjz9H6OXXsdZiwyEy555K7rgjChOOiIiIiIiIiMgBUWApIuPS8vY13LX9EQBumPMu4m50RK9nWtsJ//15Qi+/jgkCAJxFc+k49zSCisSIXltERERERETkUKLAUkTGnYyf5btr/g+LZXH9KZxYtWjErmXaOgpB5bIVPUFlftY0smedRM2x87CNHXCIr94mIiIiIiIiMpwUWIrIuPOzlXexOd1AdaiCD89824hcw7R3Ev77c4Re6htUTiV75kn40ybr7m8RERERERGREaLAUkTGlVWdm/jlmnsB+MfZV5Hw4sPav23tIHzfo4RefBXjF4PKmVMKQeX0KcN6LRERERERERHZnQJLERk38oHPf665Dd8GnFl7HG+oOWbY+jZNLYSfX07mxVcJ5/3C9aZPLgSVM6cO23VEREREREREZO8UWIrIuPE/m+5mTXIzlaEyPjrr7QfeYSZL6LXVhJa9jrtle89hf9okMt1Bpe79FhERERERERlVCixFZFx4rvU1fr/trwB86ZgPUB2qwO7PYjfW4m7cSmjZCrwVazH5fOGwMfhzZxA7/xQ6q2uwKKgUERERERERKQUFliIy5jVn2/nWml8C8KaJZ/LGySfS2NgxpD5Mazuhl18n9PLrOG29r/Vrq8gffTi5I+dDoozyugRo5W8RERERERGRklFgKSJjWmADvr3ml7TmOpgVn8KHZ75l8C/O5vCfXU708RfxNmzpOWwjYXILDyN39AKCKRN7bvvWmEoRERERERGR0lNgKSJj2h+2PcDzbSuIOCH++bD3E3HDe25sLU5DI966TbhrN+Fu3k4uCPAoDJj0Z00ld9Th5BfMhlBotN6CiIiIiIiIiAyBAksRGbNWdKzn1k13A3DdrLczMz55tzamK4m7dhPeus246zbhJFP9z9dVkV44j9xRC7CViVGpW0RERERERET2nwJLERmTuvIpvr76F/g24Kya47mo/rTCibyPv2oD4RdXFkZRNjT2e50Nefgzp5KfMwN/znRq50+jo7Fj/xboEREREREREZFRp8BSRMYcay3fW/trGjJNTPSq+FTXcUTv+xvu9kacHU3kfJ++N4b7E+vIz5mOP3s6/rRJ4LpAz9SUIiIiIiIiIjKOKLAUkbEh7+PsaMbdvpMlzU/xt7LncQP4ylPV1LU/2b9toozczKnkZ0/Hnz0NWxYvTc0iIiIiIiIiMuwUWIrI6LEWk0pjmttwWoqP1nYyrW2UbWvEBAHr4ml+cOJaAD68bgJHZKrIz6wnmFSHP6meYHI9NYdNo7O5U7d5i4iIiIiIiByEFFiKyPDyfUwyRdDehrd+WzGcbO8JKE0mu9tLLGCAdDzEvxy3gYxrOcGbwZsv/n90Vlf1u7fbGDCO7vUWEREREREROVgpsBSRwfF9bEs7ztYdmI4kprOrsO3qwulIYrqSmI4uTDKFAbJAdA9dBRXlBNUVBNWV2OpKymdOojVexveb7mXtjk6qQgk+ddR1mHDFKL5BERERERERERkLFFiKHEqCADI5TDaLk80SdLTjbm+GZBqTzmBSmcIt2+nubbpwLJ3BZLJkgMHMFmkdB6cqQa4iQVBdSVBTSVBdga2uJKisgFDvjx5jwK1L8Ojrj3LPjscA+Mzca6hRWCkiIiIiIiJySFJgKTJWBAHk/cIt1b4P+cI2yKRwdrZBLg+5PCZf3ObykO+/Nfk8WWOJdqQgk8Vkc4VbsLO5wn4+3++SWSA2lBodh6Ashi0vI0iUYcvi2EQZQXkcWx7HlpdhE2UQj1JXX0FHY8eg5pnclmzkP9fcBsCVk8/nhKqFQ6lKRERERERERA4iCizlwHUnUn23FqC4NRZbDM0I7G7tDL3HDBYbAtPe2du2p6/Cw1h697EE6SROSyc26D5fbBv0vqb7uN8QxW1NFsLB7jbFfdPzmgBjLfl4mFBHCvygt11Q2Dd99gkCjA3IhlwiXRkIfIwfFF8XFALIoPe58f3CSMVsDlMMKMn7hesPIMvgRjV2C9j3P2zrOhAO48Qi5CNhbDSKjUawsSg2Fumz33uceITaafU0DWKxGzOEKSZ96/PF539Mp59iQdlM3jf9TYN/sYiIiIiIiIgcdBRYCgCRPy4lvXYjZYGlJ2iE/vt9Uqo0lvIhrNCcAcqH0LZs8F0PKdDLMfgRhXkgMoQ6AiA0yLYWcPZ23hhwXfBcTMgjcF1syAPP67N1wQsVnoc8rOdCyKOsOkFnLsCGQ9hwCCJhbDhc2A+HsOFwoV8DdXWJQY+CHKnFbn6x8W5eallF3I3y+XnvJ+Tox5KIiIiIiIjIoUzJgIC1OE0thduGS1kGgDEYUwzsuh/F492PwjmAwnPHcwhs/zYYsMYptDMGnMK+F/LIB7a3f8eAcXq21ikcN44hHA2TyQeFa7pOsV2hrXWL+919uw5liRhd6RzWccB1i9tCu0J7t/DcdaiqTdDamcE6biFodF1sMaDEcwt9Fkuvq0vQOIRQ0atLkB9k+1JbuuMJfr/1rwB8fM67mBytK3FFIiIiIiIiIlJqCiwFjCH1gSup8aClpasQdPUNCqE3IKSQ29XUlNPc3NUTMgJYTG9A2Oe4cQy1teU0dbfv227XtvsR0A22/VBGFBoD5XUJ2ocYFOYG2bdTlyAYJ6HiSHmpbSXfW/drAD4073LOqTvhkP48RERERERERKRAgaUUuC5ObQJr3X2HRgZMRTk2awcXMBkwkTCEMn1uNZdD2eZUA19deQu+DTi79niuW/AWmpo6S12WiIiIiIiIiIwBe5tGT0Rk2LXnOvmX139Mp5/k8PJZfGruezFDWaVHRERERERERA5qCixFZNTkgjxfXXkLW9M7mRCu4cb5HybihktdloiIiIiIiIiMIQosRWRUWGv53tpf83LHauJulK8cfh3V4YpSlyUiIiIiIiIiY4wCSxEZFb/dupS/Nj6Fg+EL8z7ArPiUUpckIiIiIiIiImOQAksRGXGPNr3ArZvuBuC6WVdyYtWiElckIiIiIiIiImOVAksRGVGvd27gW6t/CcDlk87hzZPOLnFFIiIiIiIiIjKWKbAUkRGzI9PMl1//CVmb46SqI/jwzLeWuiQRERERERERGeMUWIrIiOjKp/iXFT+mJdfOrPgU/nne+3GNfuSIiIiIiIiIyN4pPRCRYefbgC889yPWJbdSHUrwlQXXEXejpS5LRERERERERMYBr9QFiMjBxVrLTzb8gcd2vETYhPjygn9gQqSm1GWJiIiIiIiIyDihEZYiMmystdyy8Q7u2v4IAJ+ddw0LymeVtigRERERERERGVc0wlJEhkX3yMo7tz8MwOeOuoYzy4/D2tLWJSIiIiIiIiLji0ZYisgBs9byw/W/7wkrPz7nnbxj1nmlLUpERERERERExiWNsBSRAxLYgJvX/Y4/73gMg+ETc97NRRNPK3VZIiIiIiIiIjJOKbAUkf0W2IDvr/sNf9nxdwyGf5r7HhbXn1rqskRERERERERkHFNgKSL7xbcBN629jft3PoWD4VNzr+a8+pNLXZaIiIiIiIiIjHMKLEVkyHwb8J01v+LBxmdwcPjsYddwTt2JpS5LRERERERERA4CCixFZEh86/Ot1b/k4abncHD4/LxrOav2+FKXJSIiIiIiIiIHCQWWIjJouSDP11fdyqNNL+Aah3+e9wHOqDm21GWJiIiIiIiIyEFEgaWIDEouyPOF537Eo00v4BmXL877IKfVHF3qskRERERERETkIKPAUkT2KRvk+PqqX/BEyzJCxuNL8z/IKdVHlbosERERERERETkIKbAUkb1qzLbybytvYUXnesJOiH+Z/yFOrDqi1GWJiIiIiIiIyEFKgaWI7NHyjjX828qf05Jrp9yN8a2TPsYcMwNrS12ZiIiIiIiIiBysFFiKyID+3PAYP1r/e/LWZ1ZsMv+y4MMcUz+HxsaOUpcmIiIiIiIiIgcxBZYi0k82yPGj9b9nyY6/A3BGzXF8au57iXuRElcmIiIiIiIiIocCBZYi0qMp28q/rfw5r3Wuw2B43/Q3cdWUCzDGlLo0ERERERERETlEKLAUEQBe7VjLV1fe0jNf5ecOu5aTqrW4joiIiIiIiIiMLgWWIsK9DY/xw+J8lTNjk7lxwYeZEq0vdVkiIiIiIiIicghSYClyCMsFeb639tfc2/A4AGfUHMs/zX0vcTda4spERERERERE5FClwFLkENWUbeOzf7+Jl1pWa75KERERERERERkzFFiKHGKstSzd+SQ/23AHnX6SsuJ8lSdrvkoRERERERERGQMUWIocQrakdvD9db/hpfaVACyomMnn5ryPKdEJJa5MRERERERERKRAgaXIISAf+Ny+7a/83+a/kLU5Ik6Iq6ddyv876s20NiexttQVioiIiIiIiIgUKLAUOci93rmem9b+mnXJLQAcV7mAG2a/iymxOjzHLXF1IiIiIiIiIiL9KbAUOUil/Az/s+lu/rT9EQIsFV4ZH575Vs6rO1kL64iIiIiIiIjImKXAUuQg9EzLcr6/9jfsyLYAcG7dSfzDzLdSFUqUuDIRERERERERkb1TYClyEGnNdfDd5/+X+7Y8CcDESC0fm30VJ1YtKnFlIiIiIiIiIiKDo8BS5CDQmU9y1/ZHuGPbg3T6KRwMV0w+l2umXUrUjZS6PBERERERERGRQVNgKTKOdeaT3Ln9Ye7Y9hBdfgqABRUz+MeZVzGvbGaJqxMRERERERERGToFliLjUGc+yR3bHuLO7Q/3BJUzYpN477RLeMuCM2lu6sLaEhcpIiIiIiIiIrIfFFiKjCMd+SR37hJUzoxN5j3TLuaMmmNxHQfHOCWuUkRERERERERk/ymwFBkHOvJJ7tj6EHdsf4iknwb6B5UKKUVERERERETkYKHAUmQMa8628/sV9/N/a+/rCSpnxSbznmmX8IaaYxRUioiIiIiIiMhBR4GlyBiTD3yeaV3OfTuf4OmW5QQEAMyKT+G9Uy/mdAWVIiIiIiIiInIQU2ApMkZsSm3nvh1P8kDjU7TkOnqOH1U9l8vrz+H0agWVIiIiIiIiInLwU2ApUkJJP83fmp5n6Y4nebVzbc/xqlCC8+pO5sIJp3LCjPk0NnZo1W8REREREREROSQosBQZZdZaXmhaye9WP8Dfmp4nHWQBcHA4qXoRF9afxslVR+I5LsaUuFgRERERERERkVGmwFJkFGSDHC+1reTp1uU83bqchkxTz7mp0QlcWH8a59WfRG24qnRFioiIiIiIiIiMAQosRUbIzkxLMaB8hRfbVpIpjqQEiLphzqw5jgvrT+eIxByMhlKKiIiIiIiIiAAKLEWGjW8Dlnes5enmV3i6dTlrk1v6na8LV3FS1RGcUn0E5885ka7WrOalFBERERERERHZhQJLkf3kW5/1yW281rGO5Z1reP7ZFbTlOnvOGwyHl8/i5OojOKXqSGbHp2KMwRiIeRG6yO6ldxERERERERGRQ5MCS5FBas918lrnel7rXMeKjnWs6Fzfs2BOt3I3xglVCzm56khOrFpEZai8RNWKiIiIiIiIiIxPCixFBuBbn1Xtm3ii4RVe7VjHax3r2Zxu2K1d3I1yePksFiZmc86MY5kaTMLBLUHFIiIiIiIiIiIHBwWWckjzbUBDpokNyW1sSG1jfXIrG1Lb2ZxqIGfzu7WfFp3IwsQsFpXPYWFiNtNjk3CNgzFQV5ugsbFD81KKiIiIiIiIiBwABZZySMgHPjtzzbzWsIZl29f2BJSbUtvJBLkBXxN3o8wvm8HCxBwWls9mYWIWCa9slCsXERERERERETm0KLCUg4JvA5oybWzPNLE900hDppnt6SYaMk1szzTRlG0lYOChj2ETYnpsIjPjk5kVm8LM+CRmxSezaOpMmpu6NGJSRERERERERGQUKbCUMc1aS9JP05JrpznXTnO2jZZcB83ZNppz7TTl2mjKt7It2Uje+nvtK2xCTC+fyPTwRGbEJjMrPpmZsclMitbhGqdfW2PA2eWYiIiIiIiIiIiMPAWWMqp8G9CVT9Ke76I930VHPknHLvup9Wm2dzbRnC2ElJldVuLeE9c4TAjXMDFSy6RoLZMitUyMFJ9H6qgJJ6ivr9A8kyIiIiIiIiIiY5gCSxkU3/qk/SzpIEsmyNLc1sKW9ia68mmS/kCPVM9+Vz5NijQt6Q66/BR2D7dm703cjVIdSlATqqQmXEF1qKKwH6lgfv00YpkYNaGq3UZK9mXMgXwCIiIiIiIiIiIyGhRYCgB3bnuYF1avoCOd7BdMpv0MmSA34IrZByLuRkl4ZSS8OBVeWb/9KVW1RHIRqr0KasKVVIcqiLmRAfsxBurqtDq3iIiIiIiIiMjBQoGlYK3l11vuozXXsc+2BkPUCRMPRYmaMHE3StyNFbcDP8q8KNNq6wi6DAm3jHIvTsgZ+FtPAaSIiIiIiIiIyKFNgaVgjOGbi25gu9lBLhkQdSJEnTARJ0zUDRN1IkScEFE3Qsh4OI4ZUqhoDNTVJmi0CiFFRERERERERGTvFFgKALPikzmxbr5GNoqIiIiIiIiISEnteYUSERERERERERERkVE2LgPLz3zmM1x99dX9jq1bt473ve99HHfccZxxxhn8+Mc/7nf+kUce4bLLLuPYY4/l4osv5qGHHhrNkkVERERERERERGQQxl1gefvtt3PPPff0O5bL5bjuuus46qijeOqpp/jpT3/KbbfdxpIlSwBYv349H/vYx/j4xz/Os88+y8c+9jE+8YlP0NDQUIq3ICIiIiIiIiIiInswrgLL1atX88Mf/pC3v/3t/Y4/88wz7NixgxtuuIFwOMyiRYu4+uqrue222wC44447OPHEEzn//PPxPI9LLrmEk046id/+9releBsiIiIiIiIiIiKyB2Nm0Z10Or3HEY/19fU4jsMnP/lJbrzxRpYtW8a6det6zq9atYrZs2cTDod7jh122GH89Kc/BQpB5/z58/v1edhhh7FixYoh12nMkF8yLnS/r8G8v6G0Ha99q47R63us1DGSfY+VOkayb9Uxen2PlTpGsm/VMXp9j5U6RrLvsVLHSPatOkav77FSx0j2rTpGr++xUsdI9q06Rq/vsVLHSPY9Vuo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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (16,8))\n",
    "sns.lineplot(data = tmp, dashes=False)\n",
    "plt.axhline(y = 0,linestyle = 'dashed',lw = 0.1,color = 'black')\n",
    "plt.xscale('log')\n",
    "plt.xticks(list_alpha)\n",
    "plt.ylabel('Coeffients',fontsize = 14)\n",
    "plt.xlabel('Alpha')\n",
    "plt.legend(loc='right')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-07T12:09:17.658035Z",
     "start_time": "2023-10-07T12:09:17.653929Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-289.51865327020374, 332.6791184233967, 34.77944193671573, -55.84478229633323, -24.166567449874186, 133.8020547715375, -19.909741728270365, -369.7426875016925, 100.54237339886168, -3.6140399229470765, 454.2056856600156, 241.57214343742672, -209.5884504988283, 78.76557723286021, 53.01946396095608, -22.423285078780822, 31.278539444237428, -58.67555869272179, -12.638577929628786]\n"
     ]
    }
   ],
   "source": [
    "#for a small value of aplpha, the coeff are - \n",
    "print(coeff_matrix[list_alpha[8]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-07T12:09:17.662969Z",
     "start_time": "2023-10-07T12:09:17.657737Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5.7633182487042996e-05, 6.404277709229968e-05, 5.0079153502888385e-05, 6.129576852892076e-05, 6.561689194262846e-05, 6.480083916119168e-05, 5.8492473598104725e-05, 7.681124525639192e-05, 8.013609785207733e-05, 7.6635366695575e-05, 8.214613025072631e-05, 8.277213810215086e-05, 7.150981326993899e-05, 4.386760495701925e-05, 3.7134612262225844e-06, -7.884609239667481e-07, -2.0850022653353344e-06, -2.810549748199537e-05, -4.1378878558150086e-07]\n"
     ]
    }
   ],
   "source": [
    "# for a large value of alpha, the coeff are - \n",
    "print(coeff_matrix[list_alpha[90]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-07T12:09:17.663985Z",
     "start_time": "2023-10-07T12:09:17.660950Z"
    }
   },
   "outputs": [],
   "source": [
    "# we can see that for large values for lambda the ceoff are very very small, although none is equal to 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-07T12:09:17.666596Z",
     "start_time": "2023-10-07T12:09:17.662913Z"
    }
   },
   "outputs": [],
   "source": [
    "list_l2_norm = []\n",
    "for alpha in list_alpha:\n",
    "    list_l2_norm.append(np.linalg.norm(coeff_matrix[alpha]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-07T12:09:17.973649Z",
     "start_time": "2023-10-07T12:09:17.669584Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "(0.0, 100.0)"
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 1400x600 with 1 Axes>",
      "image/png": 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vYOfoMSgvPQblpcegvMrVW8MeHq1ZsyZPP/10Xvva1+a2225LR0dHrrzyynz84x/P5MmT09jYuNn1DQ0NWbduXZKkra1tu+cHY+LEsYN+zuuP2C/ffWhJnlzWlsmTB/98GEkmTdIjUE56DMpLj0F56THYuwx7eFRXV5ckueqqq1JfX58xY8bkwx/+cC666KKcd9556ejo2Oz6jo6OjB7dt12ssbFxm+cnTJgw6DpWrmxJb+/gnnPomNokyWNL1mbxy6vTUFs96PeFfV2h0PdloLm5JWbLw9DTY1BeegzKS49BeVVV7dpimR0Z9vBo+vTp6e3tTVdXV+rr65Mkvf0pzlFHHZVvf/vbm12/aNGizJgxI0kyY8aMLFy4cKvzp59++qDrKBYz6L+s9h/XkKlj67O0ZX3mv7gmb3j1xEG/L4wUu9JjwM7TY1BeegzKS49BeZSrr4Z9YPbrX//6HHzwwfnkJz+Ztra2rFy5Mtdff33e+ta35qyzzsqKFSsyd+7cdHV15b777su8efMG5hxdcMEFmTdvXu677750dXVl7ty5aW5uzuzZs4el9kKhkFMO7VvldN/vVw3LewIAAABU0rCHR7W1tfnmN7+Z6urqnHnmmTnzzDMzbdq0fOYzn8mECRPy9a9/PT/+8Y9z8skn5+qrr87VV1+dU045JUly6qmn5m/+5m/y6U9/OieddFJ+9KMf5atf/WqampqGrf7X94dH9/5u5bC9JwAAAEClFIrFkblYsLl58DOPkqSlozuzb/h1eorJ7X96Ug4Y3zD0xcFerFBIJk8emxUr7GOHctBjUF56DMpLj0F5VVWVZyD9sK882tuNbajJa/cflyS57/dWHwEAAAD7NuHRLjj11f1b18w9AgAAAPZxwqNdcOqhfXdZe/CF1enu2YW9bwAAAAB7CeHRLjhy6pg0NdamrbMnj77cUulyAAAAAMpGeLQLqgqFnPyqpiTJveYeAQAAAPsw4dEu2rB17d7fmXsEAAAA7LuER7vo5EP7hmY/taw1K9d1VrgaAAAAgPIQHu2iyaPrcsR+Y5Ik97nrGgAAALCPEh7thlP6Vx8JjwAAAIB9lfBoN5y6SXjUWyxWuBoAAACAoSc82g3HHDAuo2qrs6q9K88sa610OQAAAABDTni0G2qrq3LiIU1JknttXQMAAAD2QcKj3XTqq/u2rgmPAAAAgH2R8Gg3bRia/ciStWld313hagAAAACGlvBoNx04vjGHTGhMT28x9z9v9REAAACwbxEeDYE3HT4pSXLXU8srXAkAAADA0BIeDYG3H7VfkuQXzzWnpcPWNQAAAGDfITwaAjOmjM7hk0elq6eYnz5j9REAAACw7xAeDYFCoZB3HDU1SfLjp5ZVuBoAAACAoSM8GiJnHjklSfKbF9fklbUdFa4GAAAAYGgIj4bItHENOeHg8UmSOw3OBgAAAPYRwqMh9PYj+wZn3/HE0hSLxQpXAwAAALD7hEdD6C0zp6S2upDnmtdl4fK2SpcDAAAAsNuER0NobENN3njYpCTJfzxpcDYAAACw9xMeDbF3HNW3de2up5alp9fWNQAAAGDvJjwaYq9/9cSMa6jJstbO/Hbx6kqXAwAAALBbhEdDrK6mKm+ZOTlJ8h9P2LoGAAAA7N2ER2XwjqOmJkn+c+GKdHT1VLgaAAAAgF0nPCqDYw8cl/3H1aetsye/eG5lpcsBAAAA2GXCozKoKhTy9v7B2f/xxNIKVwMAAACw64RHZbIhPPr171dl9bquClcDAAAAsGuER2Vy2KTROWK/MenpLeYnzyyvdDkAAAAAu0R4VEbv2LB17Ul3XQMAAAD2TsKjMnrbkVNSVUgeWbI2i1e3V7ocAAAAgEETHpXRlDH1OfGQpiTJ9xe8XNliAAAAAHaB8KjMLj7+wCTJbY+8nJaO7gpXAwAAADA4wqMye8NhE3P45FFp6+zJrQuWVLocAAAAgEERHpVZVaGQ9554cJLkX3/7Ujq6eipcEQAAAMDOEx4Ng7cdMSXTxtZn5bqu/OiJpZUuBwAAAGCnCY+GQU11VebMOihJ8q35i9PTW6xwRQAAAAA7R3g0TM593bSMb6jJ4tUd+c+FKypdDgAAAMBOER4Nk8ba6lx0/AFJkn954MUUi1YfAQAAAHs+4dEwuui4A9NQU5Wnl7XmgedXV7ocAAAAgB0SHg2jplG1Ofd105Ik//LgixWuBgAAAGDHhEfDbM6sg1JdSB58YXWeeKWl0uUAAAAAbJfwaJjtP64hZx61X5LkJquPAAAAgD2c8KgCLjnx4CTJfz6zIi+saq9wNQAAAAClCY8qYPrk0TntsIkpJvnWfKuPAAAAgD2X8KhC3tu/+uiHjy/Nitb1Fa4GAAAAYNuERxVy3IHjcswB49LVU8x3fruk0uUAAAAAbJPwqEIKhcLA6qPvL1iS1vXdFa4IAAAAYGvCowp64+ET8+pJo9LW2ZPvL3i50uUAAAAAbEV4VEFVhULee+JBSZLv/PalrO/urXBFAAAAAJurGewTXn755bz00kspFoubHT/xxBOHrKiR5Mwj98uNv/x9lrV25kdPLM15x+xf6ZIAAAAABgwqPLrxxhvzj//4j1sdLxQKefLJJ4esqJGktroqc2YdlOv/67l85dfPZ/bMKRnbMOhMDwAAAKAsBpVSzJ07N1/60pdyxhlnpFAolKumEeeCYw/Ivy14Oc+vas+Nv/p9rnzL9EqXBAAAAJBkkDOPampq8od/+IeCoyFWV1OVj7+1LzC69eEleeKVlgpXBAAAANBnUOHRnDlzcv3116e1tbVc9YxYJx4yIW8/ar8Uk1z3k4Xp6S3u8DkAAAAA5Tao8Oiwww7LN77xjZx44ok56qijNvth9334TYdlTH11nlzamu8veLnS5QAAAAAMbubRddddl//xP/5HXv/616e6urpcNY1Yk0bX5fLTXp2//+mi3PDL3+WMmZMzeXRdpcsCAAAARrBBhUctLS35yEc+Uq5aSHLeMftn3mOv5MmlrfnCfz2bv3uXVV0AAABA5Qxq29rs2bNz9913l6sWklRXFfLXs2ekqpDc+dTy3P/8qkqXBAAAAIxgg1p51NHRkb/8y7/M4Ycfnqamps3uunbTTTcNeXEj1VFTx+bC4w7Idx9akr//6aJ8570npK5mUDkfAAAAwJAYVHg0ffr0TJ8+vVy1sIkPvuHQ/OSZFXlhVXtuevDF/Ompr6p0SQAAAMAINKjwaNWqVfmrv/qrjBkzplz10G9MfU3+5x8elqt+9FS+cf8LeftR++WgpsZKlwUAAACMMIPaCzVv3rw0NgowhsvsI6bkpEOa0tlTzN//dFGKxWKlSwIAAABGmEGFR+eff37+9//+33n44Yfz0ksvZcmSJQM/DL1CoZAr3zI9tdWF3Pv7VfnPhSsqXRIAAAAwwgxq29o3vvGNJMn3vve9JH3hRrFYTKFQyJNPPjn01ZFXTRyV9514cL523wv5P/c8m5NfNSFj6gf1PxsAAADALhtUCvHTn/60XHWwHZeefEh+/NSyLF7dka/8+vn8zzcfXumSAAAAgBFiUNvWDjzwwDQ1NWXBggW54447Mn/+/IwdOzYHHnhgueojSX1NVT7+lr673H33oZfy9LLWClcEAAAAjBSDCo+ef/75nHXWWfnMZz6Tu+++O5///Ofz9re/PQsXLixXffQ75dCJeevMKektJp/7ycL0Gp4NAAAADINBhUef/exn8/a3vz0///nP873vfS8///nPc+655+a6664rV31s4n+++bCMrqvOoy+35N8ffaXS5QAAAAAjwKDCowULFuSv/uqvUlXV97Sqqqr85V/+ZRYsWFCW4tjclDH1+bM3HJok+dIvfpeV6zorWxAAAACwzxtUeFRdXZ3W1s3n7bS2tqaxsXFIi6K0C487IDOnjM7aju5c/1/PVbocAAAAYB83qPDozW9+cz7ykY/kueeeS2dnZ5599tl87GMfy5vf/OZy1ccWaqoK+evZM1JI8uMnl+XWh5dUuiQAAABgHzao8OgjH/lIuru78853vjPHHntszjrrrNTV1eWjH/1ouepjG167/7j8+RtfnST5h3uezfwXVle2IAAAAGCfVTOYi5uamvLNb34zL774Ypqbm3PggQdmypQp5aqN7bjkxIOyaEVb/uPJZfnEvCcyd87xOajJ9kEAAABgaO3UyqMlS5Zs9lNdXZ399tsvXV1dA8cYXoVCIVe9bWZeM21s1nR05yP//njaOrsrXRYAAACwj9mplUdnnHFGCoVCkqRYLKZQKAz8mfQFGU888UT5qmSb6muq8vlzj857v/VQnmtel/91x9P5/LlHp6r/fxcAAACA3bVT4dFPf/rTrY4Vi8V84xvfyM0335yLLrpoyAtj50wZU59/OPfofOC7C/LzZ5vz5V/9Ppef9upKlwUAAADsI3YqPDrwwAM3e7x27dp8/OMfz/z583P99dfnHe94R1mKY+e8Zv9xufrMmflfdzydb9z/Yg6fNDpnHrVfpcsCAAAA9gGDuttakjz88MM599xzs2zZsnz/+98XHO0h3nHU1Lz3xIOTJNfc9UyeXNpS4YoAAACAfcGgwqOvfe1r+eM//uOcccYZ+c53vpNDDjmkXHWxCy4/7dCcdtjErO/uzUf//fGsaOusdEkAAADAXm6nwqM1a9bkgx/8YL7yla/k+uuvz6c+9anU1dWVuzYGqbqqkGveeWRePXFUlrV25srbH8/67t5KlwUAAADsxXZq5tG5556bpUuX5t3vfneefvrpPP3001td8+d//udDXhyDN6a+Jv/n3a/Jpd9+KI++3JLP/mRh/ubMmQN3xgMAAAAYjJ0Kjw4++OAcfPDBWbx4cRYvXrzVecHEnuXgCY357FlH5S++/2h+9PjSzJg8OnNmHVTpsgAAAIC90E6FR9/85jfLXQdD7KRXTchf/eHh+Yd7ns0Xf/5cDp00Km949cRKlwUAAADsZQZ9tzX2Hhcdf0DOfd209BaTq374ZH7fvK7SJQEAAAB7mYqGRz09PbnkkkvyiU98YuDYggULcuGFF+b444/PGWeckVtuuWWz59x2222ZPXt2jjvuuJx33nl56KGHhrvsvUahUMjH3zI9xx04Lm2dPfnI7Y9nbUdXpcsCAAAA9iIVDY/+6Z/+KfPnzx94vGbNmnzgAx/Iu9/97jz44IO59tpr89nPfjaPPPJIkuT+++/PNddck+uuuy4PPvhgzjnnnFx22WVpb2+v1EfY49VWV+Vz5xydaWPr88Kq9lz1o6fS3VusdFkAAADAXqJi4dG9996bu+66K29729sGjt11111pamrKnDlzUlNTk1NPPTVnn312br755iTJLbfckne961054YQTUltbm0svvTQTJkzIHXfcUamPsVeYOKou//Du16Shpir3/X5V/u/Pn6t0SQAAAMBeYqcGZifJ3XffnQcffDCvfe1rc9ZZZ6WqamPu9OlPfzqf/vSnd/pNm5ubc9VVV+WGG27I3LlzB44vXLgwM2fO3Oza6dOn59Zbb02SLFq0KOeff/5W55966qmdfu8NCoW+n5HiyKlj8r/feUQ+/oMn8+3fvJTpk0fnnNdNq3RZ7IM29NVI6i8YTnoMykuPQXnpMSivcvXWToVH3/72t/OFL3whJ598cn7wgx9k3rx5ueGGG1JbW5sk+cEPfrDT4VFvb28+9rGP5U/+5E9y5JFHbnaura0tjY2Nmx1raGjIunXrdur8YEycOHbQz9nbXTx5bJa0decff7ow1/1kUY49bFJOeJU7sFEekyaNvB6D4aTHoLz0GJSXHoO9y06FRzfddFO+8pWv5Ljjjktzc3Pe//7355Of/GQ+//nPJ0mKxZ2fofPP//zPqauryyWXXLLVucbGxrS0tGx2rKOjI6NHjx4439HRsdX5CRMm7PT7b7ByZUt6ewf9tL3enOOm5dEXVuU/F67I+/9lfv7pgtdl5n5jKl0W+5BCoe/LQHNzSwbxVwOwk/QYlJceg/LSY1BeVVXlWSyzU+HR8uXLc9xxxyVJJk2alH/+53/OhRdemLlz5+bSSy8d1BvefvvtWbZsWWbNmpUkA2HQT37yk1x55ZX51a9+tdn1ixYtyowZM5IkM2bMyMKFC7c6f/rppw+qhiQpFjMi/7IqpJBPv+OILFnTkaeWteb9/7ognz/36Jx4yOADONiekdpjMFz0GJSXHoPy0mNQHuXqq50amD1lypSBO55tePyFL3wh//iP/5hf/epXKQxiU92Pf/zj/Pa3v838+fMzf/78nHXWWTnrrLMyf/78zJ49OytWrMjcuXPT1dWV++67L/PmzRuYc3TBBRdk3rx5ue+++9LV1ZW5c+emubk5s2fPHuTHHtkaa6tzw4XH5A8OGp+2zp78xfcfy11PLat0WQAAAMAeaKfCo/e97315//vfn6997WsDx4477rhcddVV+eAHP5j169cPSTETJkzI17/+9fz4xz/OySefnKuvvjpXX311TjnllCTJqaeemr/5m7/Jpz/96Zx00kn50Y9+lK9+9atpamoakvcfScY21OSL578ub505Od29xVz1o6dy8/zFlS4LAAAA2MMUijs5sOgnP/lJli1blve85z2bHb/zzjvzpS99KT/4wQ/KUmC5NDePzJlHW+otFvP/u+fZfPehJUmS95xwYP7yTYelyu0P2EWFQjJ58tisWGEfO5SDHoPy0mNQXnoMyquqqjwD6Xdq5lGSvPWtby15fObMmUNWEMOrqlDIR958eKaOrc8Xf/67fPs3L6W5rTP/68wjUlezUwvTAAAAgH3YbqcDK1asyDvf+c6hqIUKKRQKueTEg/O/33FEqqsKufOp5fnL2x5L6/ruSpcGAAAAVNiQLC3ZyZ1v7OHeefTU/ON/e21G1VZn/gur84HvLsjy1qGZZwUAAADsnYYkPBrM3dbYs5186IR85eJjM3FUbRYub8v/952H8/vmdZUuCwAAAKgQQ23YyhFTx+Tr7zkuh0xozMtr1+dP//XhLHhpTaXLAgAAACpgpwZmP/jggyXPrVy5csiKYc9x4PjG/L//flz+6t8fy2Mvt+SKWx/Nte86Km+aPqnSpQEAAADDaKfCo0suuWS7521b2zc1jarNDRcek0/+8Mn88rmVufIHj+fjb5me8449oNKlAQAAAMNkp8Kjp556qtx1sIdqrK3O5899Ta67e2Fuf+yVfPYni7KstTN/9vpXCQ0BAABgBDDziB2qqSrkqrfNyPtPPSRJ8v/ueyHX3rUw3b3usgcAAAD7OuERO6VQKOQDrz80fz17RqoKye2PvZKP3f542rt6Kl0aAAAAUEbCIwblvGP2z9+f85rU11Tll8+tzGXfeySr1nVWuiwAAACgTIRHDNqbpk/KDRcek/ENNXn8lZb86b8uyOLV7ZUuCwAAACgD4RG75JgDxuVrf3Rc9h9XnxdWtef/+87DeWppS6XLAgAAAIaY8IhddujEUfn6Hx2XGVNGZ+W6rvzZdx/Jfb9fWemyAAAAgCEkPGK3TB5Tn69cfGxOPKQp67p68uHbHs/N8xenx53YAAAAYJ8gPGK3jamvyT+e99qceeSU9PQW84WfPZf/8Z2Hs3B5a6VLAwAAAHaT8IghUVtdlWveeWQ+OXtGxtRX54lXWnLJtx7Kjb/8XdZ391a6PAAAAGAXCY8YMoVCIf/tmP3zvUtn5Q+nT0pPbzFfv//FzLnpN3lo8ZpKlwcAAADsAuERQ27KmPp8/tzX5HPnHJ1Jo+vy/Kr2fOC7C3LdTxamdX13pcsDAAAABkF4RNmcMWNyvnfpCTn3ddOSJN9f8HIunjs/P3+2ucKVAQAAADtLeERZjWuozdVvm5kbLzwmBzU1ZFlrZz7y74/nr+c9mea2zkqXBwAAAOyA8IhhMeuQpnznvSfkvScenOpC8pNnlueiufMz77FXUiwWK10eAAAAUILwiGHTUFudD53+6sydc3yO2G9M1nZ052/vfCZ/fuujWby6vdLlAQAAANsgPGLYHTl1bObOOT4feuOrU19TlQdeWJ3//i+/ybfmL053r1VIAAAAsCcRHlERNVWFvPekg/Od956QWQePz/ru3vzjz57L//j2Q3lmWWulywMAAAD6CY+oqIMnNOaGC4/J1W+bkTH11XlyaWvee/NDueGXv8v67t5KlwcAAAAjnvCIiisUCjn3dfvnlktn5YwZk9PTW8w37n8x77npN/nt4tWVLg8AAABGNOERe4zJY+rzuXOOzt+fc3Qmj67LC6va82fffSSfvXthWtd3V7o8AAAAGJGER+xx3jxjcr536az8t2OmJUn+7ZGXc9Hc+fnZohUVrgwAAABGHuERe6SxDTX55OyZ+fJFx+SQCY1Z3tqZj97+RD4x74msaOusdHkAAAAwYgiP2KOdcHBTbr7kD3LpSQenupD89JkVuegb8/ODR19JsVisdHkAAACwzxMescdrqK3OFW98df7lj/8gR+43Ji3ru3PNXc/k8lsfzeLV7ZUuDwAAAPZpwiP2GkfsNybfmHN8/uL0V6e+pirzX1id//4vv8lXf/18Vrd3Vbo8AAAA2CcJj9ir1FQVcsmJB+df33dCZh3SlPXdvfnKvc/nrK/cn7//6SIrkQAAAGCIFYojdHBMc3NLensrXQW7o1gs5u6nl+emBxfn6WWtSZJCkj+cMTlzTjgwxx44vrIFjlCFQjJ58tisWNGSkfm3C5SXHoPy0mNQXnoMyquqKpk0aeyQv27NkL8iDJNCoZC3HblfZh8xJfNfXJ2b57+UX/1uZe5ZuCL3LFyR1+0/Nn8866C8afrkVFcVKl0uAAAA7JWER+z1CoVCTjxkQk48ZEKea27Lt+e/lDueXJpHX27Jx+c9mQPHN+SP/uDAnP3aaRlVV13pcgEAAGCvYtsa+6QVbZ255eEl+f7DS7KmoztJMq6hJucds38uPv6ATB5TX+EK912WIkN56TEoLz0G5aXHoLzKtW1NeMQ+rb2rJz98fGm+85vFeXF1R5K+odtvP2q/zJl1UKZPHl3hCvc9vhBAeekxKC89BuWlx6C8hEdDTHg0svT0FvOLZ5vzrfmLs2DJ2oHjpxw6IX8866CcdEhTCgVzkYaCLwRQXnoMykuPQXnpMSgvA7NhN1RXFfKHMybnD2dMzqNL1ubm3yzOPQtX5L7fr8p9v1+VGVNGZ84JB+VtR05JbXVVpcsFAACAPYaVR4xYi1e3519/+1J+8Ngrae/q+z+GKWPqcvHxB+a8Y/bP2AbZ6q7wX5OgvPQYlJceg/LSY1Betq0NMeERG6xp78q/PfJyvvfQkqxo60ySNNZW5dzX7Z8/+oMDc8D4hgpXuHfxhQDKS49BeekxKC89BuUlPBpiwiO21Nndm7ueXpZvzV+cZ1esS5JUFZIzZkzJH886MK/Zf1yFK9w7+EIA5aXHoLz0GJSXHoPyMvMIyqyupipnvWZa3nX01Nz//Kp8a/7i3P/86vzkmeX5yTPLc/yB4zJn1kF54+GTUmW4NgAAACOE8Ai2UCgUcsqhE3PKoROzcHlrbv7NS7nzyWV56KW1eeilJ3LIhMa854QD866jp6ahtrrS5QIAAEBZ2bYGO2F56/p896El+bcFL6dlfXeSZHxDTS487oBcePwBmTiqrsIV7jksRYby0mNQXnoMykuPQXmZeTTEhEfsinWdPfnBY6/kO79ZnCVr1ydJ6qoLeefRUzPnhINy6KRRFa6w8nwhgPLSY1BeegzKS49BeQmPhpjwiN3R3VvMfy1ckW/NX5zHX2kZOH7aYRPzx7MOyh8cND6FEToXyRcCKC89BuWlx6C89BiUl4HZsAepqSrkrUdMyVtmTs6Cl9bm5t8szs8WNeeXz63ML59bmSP2G5PZR0zJmw6fZDUSAAAAezXhEeyGQqGQ4w4an+MOGp8XVrXn279ZnB8+vjRPL2vN08ta80+/+F0OmdCYNx0+KW+aPimv3X9cqqtG5ookAAAA9k62rcEQW72uKz95Znl+9mxz5r+wOt29G1tsQmNt3nj4xJx++OSc/KqmffJubZYiQ3npMSgvPQblpcegvMw8GmLCI4ZD6/ru3Pv7VfnZohX51e9WpnV9z8C5+pqqnPKqCTn98El54+ETM2EfuWObLwRQXnoMykuPQXnpMSgvM49gLzSmviazj5iS2UdMSXdPb367eE1+/mxzfraoOa+0rM/Pnm3Oz55tTiHJMQeMy5umT8rph0/KqyaakwQAAMCewcojqIBisZhnlrfl54v6wqOnl7Vudv7QiY05/fDJ/XOSxqZqL7pzm/+aBOWlx6C89BiUlx6D8rJtbYgJj9iTvLK2Y2BF0m8Wr0nPJnOSJo6qzRsPn5Q3HT4pJx6y589J8oUAykuPQXnpMSgvPQblJTwaYsIj9lQtHd359e9W5ufPNudXv1uZts6Nc5IaaqpyyqET8qbpk3LaqyelaVRtBSvdNl8IoLz0GJSXHoPy0mNQXmYewQgxtqEmZx61X848ar909fTmty+u6ZuNtGhFlrV25r8WNee/FjWnqpAce+D4vOnwSXnT9Ek5qKmx0qUDAACwD7LyCPYSxWIxTy9rzc/65yQtXN622fnDJo3Km6b3bW87alrl5iT5r0lQXnoMykuPQXnpMSgv29aGmPCIvd2SNf1zkp5tzkMvrk7PJp08eXRdTj98Uk6fPimzDm5KfU3VsNXlCwGUlx6D8tJjUF56DMpLeDTEhEfsS9Z2dOVXv1uZny9qzq9/tyrrujbOSRpVWz0wJ+kNr56Y8Y3lnZPkCwGUlx6D8tJjUF56DMrLzCOgpHENtXnHUVPzjqOmprO7N/NfXJ2fP9ucXzzbnGWtnfnPhSvynwtXpLqQHHfQ+JzePyfpwPHmJAEAALB9Vh7BPqxYLObJpa352bPN+fmi5ixasfmcpOmTR+f0DXOSpo5JYQjmJPmvSVBeegzKS49BeekxKC/b1oaY8IiRaPHq9vz82eb8/NnmPLx4zWZzkvYbU5c39q9ImnVwU2qrd21Oki8EUF56DMpLj0F56TEoL+HREBMeMdKtbu/Kr3+3Mj9b1Jx7f78y7V0bG2J0XXVOPXRiTnpVU46eNjaHTxqVmp0Mk3whgPLSY1BeegzKS49BeQmPhpjwCDZa392b+S+szs+eXZGfP7syzW2dm52vr6nKzCmjc/S0sTl62tgcNXVsXjWxMVXb2ObmCwGUlx6D8tJjUF56DMpLeDTEhEewbb3FYp54pSW/eLY5j7zckidfaUlbZ89W142uq84R+43ZJFAakwPHN6SqquALAZSRL91QXnoMykuPQXm52xowLKoKhbx2/3F57f7jkvSFSS+uas8TS1vy5CuteeKVljy1rDVtnT357eI1+e3iNQPPHd9Qk6Omjc2sV0/KoePrctR+Y7Pf2PpKfRQAAACGgJVHwKB19xbz++Z1eeKVljyxtCVPvNKShcvb0t279V8nk0fXDaxMOnra2Bw9dWyaRtVWoGrYN/gvtlBeegzKS49Bedm2NsSERzC0Ort7s2hFW55c2pLnVnfkt79fleea27KNPCkHjKvPUf1B0tHTxubIqWMypt5CSNgZvnRDeekxKC89BuVl2xqwR6urqcrR08bmNfuPHfhC0N7Zk6eXtebxV1ry5NK+LW8vrGrPkrXrs2Tt+vz0mRUDz3/VhMa+FUrTxuboqWNyxH5j0lBbXcFPBAAAQCI8AsqoobY6xx44PsceOH7gWOv67jy5tCVPvNLa/2dLXl67Ps+vas/zq9rzH08uS5JUF5LDJo/O0VPH5qhpfVvepk8endrqqkp9HAAAgBFJeAQMqzH1NTnxkAk58ZAJA8dWruscWJn05CsteWJpa5rbOrNweVsWLm/L7Y/1XVdbXciMKWNy9NQxfSuUpo3NqyeOSnVVoUKfBgAAYN8nPAIqbuKourzh1RPzhldPTJIUi8Usa+3sD5L67/K2tCVrO7r7hnS/0pIseDlJ0lBTlSP7h3Ef1T9D6eCmhhQKAiUAAIChIDwC9jiFQiFTx9Zn6tj6/OGMyUn6AqWX1nT0h0d9W96eWtqadV09efiltXn4pbUDzx9bXzMQKB3d/+fUsfUCJQAAgF0gPAL2CoVCIQc1Neagpsa87cj9kiQ9vcU8v2pd38qkV1ry5NKWPL2sNS3ru/PgC6vz4AurB54/cVRt/8qkMQMrlCaNrqvQpwEAANh7CI+AvVZ1VSGHTRqdwyaNzrteMzVJ0t3Tm2eb1w1sb3tyaWsWrWjLynVd+dXvVuZXv1s58PypY+tz1IYVStPG5qipYzKuobZSHwcAAGCPJDwC9ik11VU5Yr8xOWK/Mflvx+yfJOno6snC5W0Dd3d7Ymlrft+8Lktb1mdpy/r816Lmgecf3NQwsDLpqGljcuR+YzOqrrpSHwcAAKDihEfAPq+htjqvO2BcXnfAuIFjbZ3deWpp68a7vC1tyeLVHXmx/+eup5cPXDtpdF0OHN8w8HNQU2Pf700NmTS6LlVmKQEAAPsw4REwIo2uq8kJBzflhIObBo6tae/Kk0tbBgKlJ15pybLWzjS39f08smTtVq9TX1OVA8b1BUl9gVLjZkFTQ61VSwAAwN5NeATQb3xjbU45dGJOOXTiwLG1HV15aU1HXlrd0ffnmvYs7v996dqOrO/uze9WrsvvVq7b5mtO3rBqaUO4NL4xB/X/Pml0nTvAAQAAezzhEcB2jGuozbiGvju1bam7pzevtKzvD5fa+8OlvqBp8Zr2tK7vyYq2zqxo68yCUquWttwK1x80HTDOqiUAAGDPIDwC2EU11VU5qKkxBzU1Jq+asNX5tR1dA6uUNg+X2vNKy/q+VUvN6/K75m2vWpoyZtNZS42bbY2bNKrWqiUAAGBYCI8AymRcQ22Onlabo6dtZ9XS6s23wr20piOLV7enrbMny1s7s7y1Mw+/tO1VSxtXKjXmoIGtcY3Zf1y9VUsAAMCQER4BVMBmq5ay+aqlYrGYtR3dA0HSwIqlNR1Zssmqpeea1+W57axaOmh8Qw7YYoC3VUsAAMBgCY8A9jCFQiHjG2szvnHbq5a6enrzytr1eWlN+yYzljZujdt01dJD21i11FBTNbBK6cAtZi7tP74h9TVVw/ExAQCAvYTwCGAvU1tdlYMnNObgCY1bnSsWi1nTv2ppYM5S/9a4l9Z0ZGnL+nR09+bZFevy7Iptr1rar3/W0gFbbIc7cHxDJlq1BAAAI47wCGAfUigU0tRYm6bG2rymxKqllzesWtpiztKS/lVLy1o7s6zEqqXG2qqNK5aaNh/mvf84q5YAAGBfVJHw6KmnnsrnPve5PP7446mtrc0b3vCGfOITn8jEiROzYMGC/N3f/V0WLVqUCRMm5LLLLsuFF1448NzbbrstN9xwQ5YvX57DDjssn/rUp3L88cdX4mMA7HVqq6tyyITGHFJq1VJ798btcJvOXFrdt2qpvas3i1a0ZdGKtq2eX0j/HeKatt4Od2BTQyY0WrUEAAB7o0KxWCwO5xt2dHTkrW99ay666KJ88IMfTFtbWz7+8Y+nqqoqn/vc5/K2t70tf/EXf5GLL744Dz74YK644orMnTs3xxxzTO6///5cdtll+epXv5pjjjkmN998c7785S/nnnvuSWPj1v+P0PY0N7ekt7dMHxJGsEIhmTx5bFasaMnw/u1CuXV2990hbvE2tsO9tLoj67p6tvv8DauWDmpqyAHjG7LfmPpMGFWbCaNqM7GxbuD32mqrl7ZHj0F56TEoLz0G5VVVlUyatPUOhN017CuPlixZkiOPPDJXXHFFqqurU1dXl4svvjhXXnll7rrrrjQ1NWXOnDlJklNPPTVnn312br755hxzzDG55ZZb8q53vSsnnHBCkuTSSy/Nd7/73dxxxx05//zzh/ujAIwodTXbX7W0ur1rk1CpL1ha3P/7sh2sWtrUmPrqTBxVlwmNtQOB0oRRdZm45eNRtRnfUJvqKquZAACgnIY9PDrssMPyta99bbNjd955Z17zmtdk4cKFmTlz5mbnpk+fnltvvTVJsmjRoq1CounTp+epp54adB2FQt8PMLQ29JX+GlkKhUImjq7LxNF1ed0B47Y639ndm5fXdmy8O9zq9jSv68zKdV1ZNfDTmZ5i0rq+J63r2/PCqvYdv2+S8Y21mbhhBdOoujRt8XhgddOo2oytr9nrt87pMSgvPQblpcegvMrVWxUdmF0sFvOFL3wh99xzT771rW/lpptu2mr7WUNDQ9at67sjUFtb23bPD8bEiUO/jAvYqBxLJdm7HTBtfE7Yzvne3mLWdnRlRWtnVrZ1prl1fVb0/9n3uDMrWtenua3v/Kp1nSkWk9XtXVnd3pU077iGmqq+kGvSmPpMHlOXSaPrMnF0fSaNqet/XJ+JY+oyuf/YqLrqPTZs0mNQXnoMykuPwd6lYuFRa2tr/vqv/zqPP/54vvWtb+WII45IY2NjWlpaNruuo6Mjo0ePTpI0Njamo6Njq/MTJkwY9PuvXGnmEZRDodD3ZaC52T52dk1TVdI0tjaHja3d7nXdvcWsae9ftdS+cRXTynWdA6uZVvavaFq5rittnT3p7i1mWcv6LGtZv1O11NdUZcLASqa+rXJNozZ/vHF7Xd2w3G1Oj0F56TEoLz0G5VVVVZ7FMhUJj1544YW8//3vzwEHHJBbb701EydOTJLMnDkzv/rVrza7dtGiRZkxY0aSZMaMGVm4cOFW508//fRB11Asxl9WUEZ6jHKrLhQycVRdJo6qSzJ6h9d3dvdmVfvGMGl1++bh0paP13f3Zn3/kPBXdjJsGl1X3RckNdYNbJ0rNbOpqbE2Nbsxr0mPQXnpMSgvPQblUa6+GvbwaM2aNXnf+96XU045Jddee22qqjb+V9rZs2fn85//fObOnZs5c+bkN7/5TebNm5cbbrghSXLBBRfkiiuuyDve8Y6ccMIJufnmm9Pc3JzZs2cP98cAYC9TV1OVqWPrM3Vs/U5d397VM7CKaeW6rqzesKppk5Cpb9VT3+Oe3mLaOnvS1tmTxas7dvwGScY31Gw2AHzTVUwD4VP/nejGNdSkag/dQgcAwL6tUCwOb977jW98I9ddd10aGxu3miPx0EMP5dFHH821116bZ555JhMnTszll1+e8847b+Ca22+/PTfeeGOWLl2a6dOn5+qrr86xxx476Dqam21bg3Jw+1VGomKxmJb13Ru3y7VvEi5t2D7XvvHx6vauDLY9qgtJU/9d6KY2NWRMTVUmbHJXug1b6TY8Hr0Hz2uCPZl/j0F56TEor6qq8swUG/bwaE8hPILy8IUAdqynt5g1HV2bhEudA6HT6k3nNvXPdGpZ3z3o96irLmw3XNr0TnQTGmvTUFtdhk8Kex//HoPy0mNQXuUKjyp6tzUAGImqqzad17Rjnd29Wd0fJK3u6ExXdXWeX9qSlW1dWb3JsPBV/dvq2rt609lTzNKW9Vm6k/OaRtVWbxYmbRYujarNxMbNw6aa6vIPBwcAYM8gPAKAPVxdTVX2G1uf/cbW79R/sW3v6hkIk1ZusoJpyxVNG8539xazrqsn69b05KU1OzevaVxDTSY01mZ8Y98WudF1NX1/1ldndF11Rm14vMW5UZs8rhVAAQDsFYRHALCPaaytTuP46hwwvmGH1xaLfYO+V24VNnVuMq9p4+PV7V3pLSZrO7qztqM7WdW+y3XWVRf6gqT66oyqrc7o+q0Dp1F1Wx7vOzeq//cx9TVprK1O9W7cuQ4AgO0THgHACFYoFDKmviZj6mtyyITGHV7f01vM2o6Nq5fWtHcN3GWurbM76/p/b12/8fe2zp6s6+we+H19d9/Qwc6eYjrb+15rdzXWVm0WKo2ur8no2g0roTY5vsmKqK2DqZo01lYZNA4AsAXhEQCw06qr+gdxj6pLJu3aa3T39G4SKvWFTq0bfl/fPXC8tbN7s2sGwqj+a1o7e9LT27d3r72rN+1dnUnb7n2+qkLfyq3R2wiWdhRMjdnwe//xuuqCIAoA2CcIjwCAYVVTXZXxjVUZ31i7W69TLBbT2VPcuKppfU/aurr7/txktdO2gqnNAqvOvucUk/QWMxBspbVzt+qrripkzMAqp03mPtXWDMyG2tYWvTF1m8+GGl1XbUA5AFBRwiMAYK9UKBRSX1NIfU1dJozavdcqFovp6O5N2/rNQ6WN2/A233q3vWBqXVdPkr4tfms6urOmozvJzt31rpT6mqr+uVBbbsPbciB5Tf8qqE0CqP6ZUuZDAQC7SngEAIx4hUKhb9B4bXUm7+Zr9fQW0961cQ5U2/rSq53Wde3cfKj13b1Z3907pPOhtrfaacPvYwaGmW8Mrcb0P6ehxnwoABgphEcAAEOoumrjEPKkfrdea1fnQ20IprY3H2rFEMyHGlW3Rai0yaqo7QVTYzasiKozHwoA9gbCIwCAPdSePh+qdX3flr7d/pxVhY3b8OprMqq2L1iqr6na5Kc6ddVVqa+tSkNNVd/vNVWpq9nkcW3fnw39x+tr+l9jk3O27QHA4AmPAAD2ccMxH2rjNrzBz4fqHsL5UDtSU1UYCKQ2BFCb/tTVVGXcqPqktyf11dX9IdTGkGpbz9twTX31xsCqbtPzQisA9nLCIwAAdtpwzYfaMPOps6d3YObT+u7edG74vWfTxz2bX7PFc7r7t+wlfUFV94Y76g2jTUOrzcOr6v5gr3qLEGrzAKpU4LXVCqtNV2PVVKXKdkAAhoDwCACAihjK+VDb09Nb7AuUurYROvVsHkp19vSmpqEuzavXpbO7Nx2bnuvuTUd3Tzp7igOB1VbX9OxZoVVtdWEbK6w2bgHcKqiq3jKY2nHY1bBFqCW0Atj3CI8AANinVVcV0ljVt1pqRwqFZPLksVmxoiXF4g4v367u3mK6+kOrTUOngcCp/1xnT9/jrVZWdW0Io3pKrqraMvjq6O4dGI6eJF09xXT1VCa02hA4NWwIrLbYArjNuVVbXrNJ2NWwjUCrfovXEVoBlIfwCAAAyqCmqpCagdBq94aeD0Z3bzGdm6yU2pntf5tvAewPubZ8znaCq/U92w6tkuENreqqCxu38lUXtjmDquS8q+qq1NdufN5Wq7Fqt7FtsP+xuwUC+zrhEQAA7ENqqgqpqeu7Y10lQqsdzaDaOrjq2e72v+0HVz3p2WSFWGdPMZ09Q3MXwMGo2zJw2s7sqo0B1E7Mu9qwvXCTVVx1/b/XGsQODCPhEQAAsNs2D62GT3fvxu2A67cRQm09t2rr7YDb2v7XuUV41dG1+byr3q1Cq+5y3yxwK4UkNdWF1FZVpaa60L/arZDa6qq+3zc5V1tVSHX/8dqqQmqqq/r/3PL5G6/vO1aV2g3nNrzuJu+x4VztJudqBs5ten3fa9XVFNLV05tisdj/CYC9gfAIAADYa/WFVjUZXTe879vds/kA9m3OrdrGdsCdv4vgtuddbRpaFbPJFsGu4f38Q6FUEFU6pNo85Np4/aYB2Cbh1/ZCsi3Dr5LnNv99w/W2KjLSCI8AAAAGqaa6KjXVVcMaWhWLxfT0FrO+pzfdPcV09RbT3dN3Z7++x1v8PnBNMT29venqKQ4Mcu/u3fr53Ztcs9l1/TOtNp7b+rW6N3mtbZ3bdCbWBhvOdXT3Dt8/xCFSvSGc2lb4tUkQtVX41f979VYrs0qEZBvCry1WlG1vhdj2grDa6oLB8uwS4REAAMBeoFDoDwqqqypdyqD19gdf3b3FjG8anaXL16Zzy8BqG0FYyXNbhGddmwRg3VuGZBuObyMk2174tel1W+rpD8T6dioO74yt3VVVyBZh1Nbh185sVdzuCrFthVrbuH6rkGyLrZZbBmnVhVj1VSHCIwAAAMqqqlBIVXUhdTXJ+FG16Rpdl+LWmcweqbhJ8LVpELX9AKp0EFU6CNs85CoVkvX09A5c37WNsGzLkGzLf8y9xY3D5fc2G+Z8bWvO1q7O6aouEX5t+RrbmuG1vdVmW80c28u3OwqPAAAAoISNK76ShuG7geGQ6RnE9sKuLbYxbhqYbXp9zxbXbxqMdW3zfQYXkm14fs8WydfGOV/FtHftfdsdN4ZdW6yw6r974u4Os6+tKmR0fU0un33E0Nc+5K8IAAAA7BGqqwqprhreuyAOld7idlZYbXhcYqtiz3bCr53Zqti9jVBty9fo2ercxiBse3O+0p2Ua7vjGOERAAAAMFJUFQqpqymkLnvfnK9icevh81uutOrZMtja1iqs7Q6z33zOV3dvb6qry7M1TngEAAAAMIQKhb7ZSLXVSWPt8K38qipTzrb3xXcAAAAADBvhEQAAAAAlCY8AAAAAKEl4BAAAAEBJwiMAAAAAShIeAQAAAFCS8AgAAACAkoRHAAAAAJQkPAIAAACgJOERAAAAACUJjwAAAAAoSXgEAAAAQEnCIwAAAABKEh4BAAAAUJLwCAAAAICShEcAAAAAlCQ8AgAAAKAk4REAAAAAJQmPAAAAAChJeAQAAABAScIjAAAAAEoSHgEAAABQkvAIAAAAgJKERwAAAACUJDwCAAAAoCThEQAAAAAlCY8AAAAAKEl4BAAAAEBJwiMAAAAAShIeAQAAAFCS8AgAAACAkoRHAAAAAJQkPAIAAACgJOERAAAAACUJjwAAAAAoSXgEAAAAQEnCIwAAAABKEh4BAAAAUJLwCAAAAICShEcAAAAAlCQ8AgAAAKAk4REAAAAAJQmPAAAAAChJeAQAAABAScIjAAAAAEoSHgEAAABQkvAIAAAAgJKERwAAAACUJDwCAAAAoCThEQAAAAAlCY8AAAAAKEl4BAAAAEBJwiMAAAAAShIeAQAAAFCS8AgAAACAkoRHAAAAAJQkPAIAAACgJOERAAAAACUJjwAAAAAoSXgEAAAAQEnCIwAAAABKEh4BAAAAUJLwCAAAAICS9srwqLm5OZdffnlmzZqVk08+Oddee226u7srXRYAAADAPmevDI8+/OEPZ9SoUfnFL36RW2+9Nffee2/mzp1b6bIAAAAA9jl7XXj0/PPP54EHHsjHPvaxNDY25uCDD87ll1+em2++udKlAQAAAOxzaipdwGAtXLgwTU1NmTp16sCxww8/PEuWLMnatWszbty4nXqdQqHvBxhaG/pKf0F56DEoLz0G5aXHoLzK1Vt7XXjU1taWxsbGzY5teLxu3bqdDo8mThw75LUBG02apMegnPQYlJceg/LSY7B32evCo1GjRqW9vX2zYxsejx49eqdfZ+XKlvT2DmlpQPqS7kmTxqa5uSXFYqWrgX2PHoPy0mNQXnoMyquqqjyLZfa68GjGjBlZvXp1VqxYkcmTJydJnn322UybNi1jx+78P6BiMf6ygjLSY1BeegzKS49BeekxKI9y9dVeNzD70EMPzQknnJDPfOYzaW1tzYsvvpgbbrghF1xwQaVLAwAAANjn7HXhUZJ88YtfTHd3d97ylrfkoosuyhvf+MZcfvnllS4LAAAAYJ+z121bS5LJkyfni1/8YqXLAAAAANjn7ZUrjwAAAAAYHsIjAAAAAEoSHgEAAABQkvAIAAAAgJKERwAAAACUJDwCAAAAoCThEQAAAAAlCY8AAAAAKEl4BAAAAEBJwiMAAAAAShIeAQAAAFCS8AgAAACAkoRHAAAAAJQkPAIAAACgpJpKF1AphULfDzC0NvSV/oLy0GNQXnoMykuPQXmVq7cKxWKxWJ6XBgAAAGBvZ9saAAAAACUJjwAAAAAoSXgEAAAAQEnCIwAAAABKEh4BAAAAUJLwCAAAAICShEcAAAAAlCQ8AgAAAKAk4REAAAAAJY2Y8Ki5uTmXX355Zs2alZNPPjnXXntturu7K10W7LWeeuqp/Mmf/ElOOumkvOENb8iVV16ZlStXJkkWLFiQCy+8MMcff3zOOOOM3HLLLRWuFvZePT09ueSSS/KJT3xi4Jgeg923evXqXHnllTn55JNz4okn5vLLL8+yZcuS6DEYCo8//njmzJmTWbNm5bTTTsvf/d3fpbOzM4keg92xcuXKzJ49O/fff//AsR311G233ZbZs2fnuOOOy3nnnZeHHnpo0O87YsKjD3/4wxk1alR+8Ytf5NZbb829996buXPnVros2Ct1dHTkT//0T3P88cfnl7/8ZX74wx9m9erV+eQnP5k1a9bkAx/4QN797nfnwQcfzLXXXpvPfvazeeSRRypdNuyV/umf/inz588feKzHYGh86EMfyrp163L33XfnnnvuSXV1dT71qU/pMRgCvb29+bM/+7OceeaZeeCBB3Lrrbfml7/8Zb761a/qMdgNv/nNb3LxxRfnhRdeGDi2o566//77c8011+S6667Lgw8+mHPOOSeXXXZZ2tvbB/XeIyI8ev755/PAAw/kYx/7WBobG3PwwQfn8ssvz80331zp0mCvtGTJkhx55JG54oorUldXlwkTJuTiiy/Ogw8+mLvuuitNTU2ZM2dOampqcuqpp+bss8/Wb7AL7r333tx1111529veNnBMj8Hue+yxx7JgwYJcd911GTduXMaMGZNrrrkmH/3oR/UYDIE1a9Zk+fLl6e3tTbFYTJJUVVWlsbFRj8Euuu222/LRj340f/VXf7XZ8R311C233JJ3vetdOeGEE1JbW5tLL700EyZMyB133DGo9x8R4dHChQvT1NSUqVOnDhw7/PDDs2TJkqxdu7aClcHe6bDDDsvXvva1VFdXDxy7884785rXvCYLFy7MzJkzN7t++vTpeeqpp4a7TNirNTc356qrrsr/+T//J42NjQPH9RjsvkceeSTTp0/P9773vcyePTunnXZaPve5z2XKlCl6DIbAhAkTcumll+Zzn/tcXve61+VNb3pTDj300Fx66aV6DHbRaaedlrvvvjvvfOc7Nzu+o55atGjRkPTciAiP2traNvvinWTg8bp16ypREuwzisVirr/++txzzz256qqrttlvDQ0Neg0Gobe3Nx/72MfyJ3/yJznyyCM3O6fHYPetWbMmTz/9dH7/+9/ntttuy7//+79n6dKl+fjHP67HYAj09vamoaEhn/rUp/Lwww/nhz/8YZ599tl88Ytf1GOwi6ZMmZKampqtju+op4aq50ZEeDRq1Kit9vNteDx69OhKlAT7hNbW1vzFX/xF5s2bl29961s54ogj0tjYmI6Ojs2u6+jo0GswCP/8z/+curq6XHLJJVud02Ow++rq6pIkV111VcaMGZPJkyfnwx/+cH72s5+lWCzqMdhNd999d+6888685z3vSV1dXWbMmJErrrgi3/nOd/x7DIbYjnpqqHpuRIRHM2bMyOrVq7NixYqBY88++2ymTZuWsWPHVrAy2Hu98MILOf/889Pa2ppbb701RxxxRJJk5syZWbhw4WbXLlq0KDNmzKhEmbBXuv322/PAAw9k1qxZmTVrVn74wx/mhz/8YWbNmqXHYAhMnz49vb296erqGjjW29ubJDnqqKP0GOyml19+eeDOahvU1NSktrbWv8dgiO2op2bMmDEkPTciwqNDDz00J5xwQj7zmc+ktbU1L774Ym644YZccMEFlS4N9kpr1qzJ+973vvzBH/xB/t//+3+ZOHHiwLnZs2dnxYoVmTt3brq6unLfffdl3rx5Of/88ytYMexdfvzjH+e3v/1t5s+fn/nz5+ess87KWWedlfnz5+sxGAKvf/3rc/DBB+eTn/xk2trasnLlylx//fV561vfmrPOOkuPwW467bTTsnz58nz5y19OT09PXnzxxdx44405++yz/XsMhtiOeuqCCy7IvHnzct9996Wrqytz585Nc3NzZs+ePaj3KRQ3jL/fx61YsSJ/+7d/m/vvvz9VVVV597vfnY9+9KObDfwFds43vvGNXHfddWlsbEyhUNjs3EMPPZRHH3001157bZ555plMnDgxl19+ec4777wKVQt7v0984hNJkuuuuy5J9BgMgaVLlw7ctnj9+vU544wzctVVV2XcuHF6DIbAr3/963zhC1/Ic889l7Fjx+acc84ZuFOvHoPdc8QRR+Smm27KySefnGTH3w1vv/323HjjjVm6dGmmT5+eq6++Oscee+yg3nPEhEcAAAAADN6I2LYGAAAAwK4RHgEAAABQkvAIAAAAgJKERwAAAACUJDwCAAAAoCThEQAAAAAlCY8AAAAAKEl4BACwE26++eYcccQRmTt37mbHzzjjjPzbv/3bDp+/ePHiHHHEEVm8eHGZKgQAKA/hEQDATrj55pvzR3/0R7npppvS3d1d6XIAAIaN8AgAYAfuvffeNDc35xOf+ER6e3tz5513bvO6Sy65JNddd13OO++8HHfccTnvvPMyf/78za6ZN29e3vGOd+S4447LpZdemqVLlyZJisVivvKVr+Tss8/OrFmzcuKJJ+YjH/lIOjo6yv75AAC2R3gEALAD3/zmN3PRRReloaEh73nPe/L1r3+95LXf/e53c+WVV+aBBx7I7Nmzc9lll2XVqlUD5x9//PF873vfy89+9rOsWbMmX/rSl5Ik//Ef/5Gbbrop//f//t/Mnz8///qv/5pf/vKXmTdvXtk/HwDA9giPAAC246WXXsovfvGLzJkzJ0ly0UUXZdGiRXnggQe2ef3555+fU045JXV1dfngBz+YxsbG3HPPPQPnP/jBD2bs2LEZP3583vjGN+aFF15Ikpx++um59dZbc+ihh2blypVZtWpVmpqaBlYmAQBUSk2lCwAA2JN9+9vfTnd3d84999yBY93d3fn617+ek046aavrDz300IHfC4VCpk2bluXLlw8ca2pqGvi9trY2PT09Sfq2rV1//fW55557MnHixBx11FHp6upKsVgc+g8FADAIwiMAgBLWr1+fW2+9Nddee21e//rXDxx/5pln8oEPfCDPPvvsVs/ZdKVQb29vlixZkv3333+H7/UP//APWbJkSf7zP/8zY8aMSZKcffbZQ/ApAAB2j21rAAAlzJs3L4VCIWeffXamTZs28HP66adn5syZmTt37lbPueWWW/LYY4+ls7MzX/rSl1IsFvPmN795h+/V2tqa+vr6VFdXZ/369fn617+eZ555Jl1dXWX4ZAAAO094BABQwre//e2cffbZqa2t3ercxRdfnNtvvz3Nzc2bHT/ppJPyt3/7tznllFNy//335+tf/3rGjh27w/f68Ic/nI6Ojrz+9a/PGWeckYcffjjnnntunnnmmSH7PAAAu6JQtJEeAGBIXHLJJTnppJPyoQ99qNKlAAAMGSuPAAAAAChJeAQAAABASbatAQAAAFCSlUcAAAAAlCQ8AgAAAKAk4REAAAAAJQmPAAAAAChJeAQAAABAScIjAAAAAEoSHgEAAABQkvAIAAAAgJKERwAAAACU9P8HKJqLPmpcDPkAAAAASUVORK5CYII="
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (14,6))\n",
    "plt.plot(list_alpha,list_l2_norm)\n",
    "plt.xlabel('Alpha')\n",
    "plt.ylabel('L2 Norm')\n",
    "plt.xlim(0,100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-07T12:09:17.973926Z",
     "start_time": "2023-10-07T12:09:17.780723Z"
    }
   },
   "outputs": [],
   "source": [
    "# this is the relationship between, l2 norm, and alpha, We can see that as X increases, l2 norm decreases. Which means \n",
    "# the size of the coefficients are decreasing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Selecting the best value of alpha"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-07T12:09:17.974003Z",
     "start_time": "2023-10-07T12:09:17.783463Z"
    }
   },
   "outputs": [],
   "source": [
    "# Using validation approach\n",
    "X_train,X_test,y_train,y_test = train_test_split(data.drop('Salary',axis = 1),data['Salary'],test_size = 0.3,random_state = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-07T12:09:18.073929Z",
     "start_time": "2023-10-07T12:09:17.795298Z"
    }
   },
   "outputs": [],
   "source": [
    "validation_score = []\n",
    "for alpha in list_alpha:\n",
    "    model = Ridge(alpha=alpha)\n",
    "    model.fit(X_train,y_train)\n",
    "    validation_score.append(mean_squared_error(model.predict(X_test),y_test)*len(y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-07T12:09:18.250962Z",
     "start_time": "2023-10-07T12:09:17.952996Z"
    }
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "lineplot() takes from 0 to 1 positional arguments but 2 were given",
     "output_type": "error",
     "traceback": [
      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[0;31mTypeError\u001B[0m                                 Traceback (most recent call last)",
      "Cell \u001B[0;32mIn[20], line 1\u001B[0m\n\u001B[0;32m----> 1\u001B[0m \u001B[43msns\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mlineplot\u001B[49m\u001B[43m(\u001B[49m\u001B[43mlist_alpha\u001B[49m\u001B[43m,\u001B[49m\u001B[43mvalidation_score\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m      2\u001B[0m plt\u001B[38;5;241m.\u001B[39mxscale(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mlog\u001B[39m\u001B[38;5;124m'\u001B[39m)\n",
      "\u001B[0;31mTypeError\u001B[0m: lineplot() takes from 0 to 1 positional arguments but 2 were given"
     ]
    }
   ],
   "source": [
    "sns.lineplot(list_alpha,validation_score)\n",
    "plt.xscale('log')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-07T12:09:18.252183Z",
     "start_time": "2023-10-07T12:09:18.251718Z"
    }
   },
   "outputs": [],
   "source": [
    "np.argmin(validation_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lasso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-07T12:09:18.253424Z",
     "start_time": "2023-10-07T12:09:18.253297Z"
    }
   },
   "outputs": [],
   "source": [
    "list_alpha = 10**np.linspace(-2,10,100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2023-10-07T12:09:18.254493Z"
    }
   },
   "outputs": [],
   "source": [
    "# in R, by default, the predictors are standardized\n",
    "X = data.drop('Salary',axis = 1)\n",
    "y = data['Salary']\n",
    "scaler = StandardScaler()\n",
    "X = scaler.fit_transform(X)\n",
    "# Now, X is standardised"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2023-10-07T12:09:18.255693Z"
    }
   },
   "outputs": [],
   "source": [
    "coeff_matrix = {}\n",
    "\n",
    "for alpha in list_alpha:\n",
    "    model = Lasso(alpha=alpha)\n",
    "    model.fit(X,y)\n",
    "    coeff_matrix[alpha] = list(model.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2023-10-07T12:09:18.256769Z"
    }
   },
   "outputs": [],
   "source": [
    "tmp = pd.DataFrame(coeff_matrix).T\n",
    "tmp.index = list_alpha\n",
    "tmp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2023-10-07T12:09:18.257971Z"
    }
   },
   "outputs": [],
   "source": [
    "plt.figure(figsize = (16,8))\n",
    "sns.lineplot(data = tmp, dashes=False)\n",
    "plt.axhline(y = 0,linestyle = 'dashed',lw = 0.1,color = 'black')\n",
    "plt.xscale('log')\n",
    "plt.xticks(list_alpha)\n",
    "plt.ylabel('Coeffients',fontsize = 14)\n",
    "plt.xlabel('Alpha')\n",
    "plt.legend(loc='right')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2023-10-07T12:09:18.259040Z"
    }
   },
   "outputs": [],
   "source": [
    "#for a small value of aplpha, the coeff are - \n",
    "print(coeff_matrix[list_alpha[8]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2023-10-07T12:09:18.260101Z"
    }
   },
   "outputs": [],
   "source": [
    "# for a large value of alpha, the coeff are - \n",
    "print(coeff_matrix[list_alpha[90]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2023-10-07T12:09:18.261731Z"
    }
   },
   "outputs": [],
   "source": [
    "# we can see that for large values for lambda the ceoff are exactly zero, we can compare this with what we got from ridge."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2023-10-07T12:09:18.263103Z"
    }
   },
   "outputs": [],
   "source": [
    "list_l2_norm = []\n",
    "for alpha in list_alpha:\n",
    "    list_l2_norm.append(np.linalg.norm(coeff_matrix[alpha]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2023-10-07T12:09:18.263817Z"
    }
   },
   "outputs": [],
   "source": [
    "plt.figure(figsize = (14,6))\n",
    "plt.plot(list_alpha,list_l2_norm)\n",
    "plt.xlabel('Alpha')\n",
    "plt.ylabel('L2 Norm')\n",
    "plt.xlim(0,100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2023-10-07T12:09:18.264645Z"
    }
   },
   "outputs": [],
   "source": [
    "# this is the relationship between, l2 norm, and alpha, We can see that as X increases, l2 norm decreases. Which means \n",
    "# the size of the coefficients are decreasing, but the decrease is not that smooth as it was in the case of Ridge regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Selecting the best value of alpha, this time using cross validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2023-10-07T12:09:18.265281Z"
    }
   },
   "outputs": [],
   "source": [
    "parameters = {'alpha':list_alpha}\n",
    "lasso = Lasso()\n",
    "model = GridSearchCV(lasso,parameters)\n",
    "model.fit(data.drop('Salary',axis = 1),data['Salary'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-07T12:09:18.288227Z",
     "start_time": "2023-10-07T12:09:18.266056Z"
    }
   },
   "outputs": [],
   "source": [
    "best_model = model.best_estimator_\n",
    "best_model\n",
    "# value of alpha is 174.75"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2023-10-07T12:09:18.267325Z"
    }
   },
   "outputs": [],
   "source": [
    "# this is the model that is choosen\n",
    "best_model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2023-10-07T12:09:18.268176Z"
    }
   },
   "outputs": [],
   "source": [
    "# We can see that only 10 coeffficients are having non zero weights. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2023-10-07T12:09:18.268847Z"
    }
   },
   "outputs": [],
   "source": [
    "pd.DataFrame({'Coefficients':list(best_model.coef_)},index = list(data.drop('Salary',axis = 1).columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2023-10-07T12:09:18.269444Z"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2023-10-07T12:09:18.270148Z"
    }
   },
   "outputs": [],
   "source": []
  }
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